Overview

Dataset statistics

Number of variables42
Number of observations5000
Missing cells50471
Missing cells (%)24.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory370.4 B

Variable types

Categorical22
Numeric17
Boolean3

Alerts

restaurant_link has a high cardinality: 5000 distinct valuesHigh cardinality
restaurant_name has a high cardinality: 4864 distinct valuesHigh cardinality
original_location has a high cardinality: 3157 distinct valuesHigh cardinality
region has a high cardinality: 218 distinct valuesHigh cardinality
province has a high cardinality: 536 distinct valuesHigh cardinality
city has a high cardinality: 2004 distinct valuesHigh cardinality
address has a high cardinality: 4999 distinct valuesHigh cardinality
awards has a high cardinality: 176 distinct valuesHigh cardinality
popularity_detailed has a high cardinality: 4585 distinct valuesHigh cardinality
popularity_generic has a high cardinality: 4574 distinct valuesHigh cardinality
top_tags has a high cardinality: 1657 distinct valuesHigh cardinality
price_range has a high cardinality: 493 distinct valuesHigh cardinality
meals has a high cardinality: 153 distinct valuesHigh cardinality
cuisines has a high cardinality: 1566 distinct valuesHigh cardinality
features has a high cardinality: 637 distinct valuesHigh cardinality
original_open_hours has a high cardinality: 2264 distinct valuesHigh cardinality
keywords has a high cardinality: 451 distinct valuesHigh cardinality
latitude is highly overall correlated with countryHigh correlation
longitude is highly overall correlated with countryHigh correlation
open_days_per_week is highly overall correlated with open_hours_per_weekHigh correlation
open_hours_per_week is highly overall correlated with open_days_per_weekHigh correlation
avg_rating is highly overall correlated with food and 3 other fieldsHigh correlation
total_reviews_count is highly overall correlated with reviews_count_in_default_language and 3 other fieldsHigh correlation
reviews_count_in_default_language is highly overall correlated with total_reviews_count and 5 other fieldsHigh correlation
excellent is highly overall correlated with total_reviews_count and 5 other fieldsHigh correlation
very_good is highly overall correlated with total_reviews_count and 5 other fieldsHigh correlation
average is highly overall correlated with total_reviews_count and 5 other fieldsHigh correlation
poor is highly overall correlated with reviews_count_in_default_language and 4 other fieldsHigh correlation
terrible is highly overall correlated with reviews_count_in_default_language and 4 other fieldsHigh correlation
food is highly overall correlated with avg_rating and 3 other fieldsHigh correlation
service is highly overall correlated with avg_rating and 3 other fieldsHigh correlation
value is highly overall correlated with avg_rating and 3 other fieldsHigh correlation
atmosphere is highly overall correlated with avg_rating and 3 other fieldsHigh correlation
country is highly overall correlated with latitude and 1 other fieldsHigh correlation
special_diets is highly overall correlated with vegetarian_friendly and 2 other fieldsHigh correlation
vegetarian_friendly is highly overall correlated with special_diets and 1 other fieldsHigh correlation
vegan_options is highly overall correlated with special_diets and 2 other fieldsHigh correlation
gluten_free is highly overall correlated with special_diets and 1 other fieldsHigh correlation
special_diets is highly imbalanced (51.5%)Imbalance
region has 227 (4.5%) missing valuesMissing
province has 1546 (30.9%) missing valuesMissing
city has 1871 (37.4%) missing valuesMissing
latitude has 69 (1.4%) missing valuesMissing
longitude has 69 (1.4%) missing valuesMissing
awards has 3774 (75.5%) missing valuesMissing
popularity_detailed has 415 (8.3%) missing valuesMissing
popularity_generic has 426 (8.5%) missing valuesMissing
top_tags has 460 (9.2%) missing valuesMissing
price_level has 1211 (24.2%) missing valuesMissing
price_range has 3592 (71.8%) missing valuesMissing
meals has 1989 (39.8%) missing valuesMissing
cuisines has 761 (15.2%) missing valuesMissing
special_diets has 3367 (67.3%) missing valuesMissing
features has 3527 (70.5%) missing valuesMissing
original_open_hours has 2222 (44.4%) missing valuesMissing
open_days_per_week has 2222 (44.4%) missing valuesMissing
open_hours_per_week has 2222 (44.4%) missing valuesMissing
working_shifts_per_week has 2222 (44.4%) missing valuesMissing
avg_rating has 419 (8.4%) missing valuesMissing
total_reviews_count has 234 (4.7%) missing valuesMissing
default_language has 416 (8.3%) missing valuesMissing
reviews_count_in_default_language has 416 (8.3%) missing valuesMissing
excellent has 416 (8.3%) missing valuesMissing
very_good has 416 (8.3%) missing valuesMissing
average has 416 (8.3%) missing valuesMissing
poor has 416 (8.3%) missing valuesMissing
terrible has 416 (8.3%) missing valuesMissing
food has 2130 (42.6%) missing valuesMissing
service has 2101 (42.0%) missing valuesMissing
value has 2115 (42.3%) missing valuesMissing
atmosphere has 3812 (76.2%) missing valuesMissing
keywords has 4549 (91.0%) missing valuesMissing
restaurant_link is uniformly distributedUniform
restaurant_name is uniformly distributedUniform
address is uniformly distributedUniform
popularity_detailed is uniformly distributedUniform
popularity_generic is uniformly distributedUniform
keywords is uniformly distributedUniform
restaurant_link has unique valuesUnique
total_reviews_count has 185 (3.7%) zerosZeros
excellent has 682 (13.6%) zerosZeros
very_good has 1231 (24.6%) zerosZeros
average has 2249 (45.0%) zerosZeros
poor has 2873 (57.5%) zerosZeros
terrible has 2632 (52.6%) zerosZeros

Reproduction

Analysis started2023-01-27 07:27:14.561816
Analysis finished2023-01-27 07:27:48.993694
Duration34.43 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

restaurant_link
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
g186402-d20259720
 
1
g17457838-d17453748
 
1
g187472-d2077530
 
1
g504140-d3175652
 
1
g187253-d2627923
 
1
Other values (4995)
4995 

Length

Max length19
Median length18
Mean length16.7382
Min length15

Characters and Unicode

Total characters83691
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5000 ?
Unique (%)100.0%

Sample

1st rowg186402-d20259720
2nd rowg790189-d10396421
3rd rowg3236223-d23258847
4th rowg13091931-d13089780
5th rowg3793652-d14039519

Common Values

ValueCountFrequency (%)
g186402-d20259720 1
 
< 0.1%
g17457838-d17453748 1
 
< 0.1%
g187472-d2077530 1
 
< 0.1%
g504140-d3175652 1
 
< 0.1%
g187253-d2627923 1
 
< 0.1%
g503717-d11805592 1
 
< 0.1%
g673469-d19321753 1
 
< 0.1%
g186220-d6765646 1
 
< 0.1%
g186338-d15705904 1
 
< 0.1%
g187448-d9847699 1
 
< 0.1%
Other values (4990) 4990
99.8%

Length

2023-01-27T10:27:49.067722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
g186402-d20259720 1
 
< 0.1%
g186400-d12519595 1
 
< 0.1%
g3793652-d14039519 1
 
< 0.1%
g186356-d13526221 1
 
< 0.1%
g187111-d3423656 1
 
< 0.1%
g4922900-d9803410 1
 
< 0.1%
g187342-d17295079 1
 
< 0.1%
g503747-d4097586 1
 
< 0.1%
g1080461-d5821803 1
 
< 0.1%
g635839-d11949331 1
 
< 0.1%
Other values (4990) 4990
99.8%

Most occurring characters

ValueCountFrequency (%)
1 10864
13.0%
8 7680
9.2%
7 7183
8.6%
2 6710
8.0%
4 6316
7.5%
6 6196
7.4%
3 6060
7.2%
0 5922
 
7.1%
9 5913
 
7.1%
5 5847
 
7.0%
Other values (3) 15000
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68691
82.1%
Lowercase Letter 10000
 
11.9%
Dash Punctuation 5000
 
6.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10864
15.8%
8 7680
11.2%
7 7183
10.5%
2 6710
9.8%
4 6316
9.2%
6 6196
9.0%
3 6060
8.8%
0 5922
8.6%
9 5913
8.6%
5 5847
8.5%
Lowercase Letter
ValueCountFrequency (%)
g 5000
50.0%
d 5000
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73691
88.1%
Latin 10000
 
11.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10864
14.7%
8 7680
10.4%
7 7183
9.7%
2 6710
9.1%
4 6316
8.6%
6 6196
8.4%
3 6060
8.2%
0 5922
8.0%
9 5913
8.0%
5 5847
7.9%
Latin
ValueCountFrequency (%)
g 5000
50.0%
d 5000
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10864
13.0%
8 7680
9.2%
7 7183
8.6%
2 6710
8.0%
4 6316
7.5%
6 6196
7.4%
3 6060
7.2%
0 5922
 
7.1%
9 5913
 
7.1%
5 5847
 
7.0%
Other values (3) 15000
17.9%

restaurant_name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct4864
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
McDonald's
 
22
Subway
 
21
Burger King
 
13
Domino's Pizza
 
10
KFC
 
7
Other values (4859)
4927 

Length

Max length66
Median length46
Mean length16.3264
Min length2

Characters and Unicode

Total characters81632
Distinct characters166
Distinct categories15 ?
Distinct scripts5 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4812 ?
Unique (%)96.2%

Sample

1st rowThe Coffee Bar
2nd rowEl Griego
3rd rowRestaurant Karavovrisi
4th rowAroma Cafe-Bar
5th rowVanity caffè

Common Values

ValueCountFrequency (%)
McDonald's 22
 
0.4%
Subway 21
 
0.4%
Burger King 13
 
0.3%
Domino's Pizza 10
 
0.2%
KFC 7
 
0.1%
Starbucks 5
 
0.1%
O'Tacos 4
 
0.1%
L'Annexe 4
 
0.1%
Costa Coffee 4
 
0.1%
Papa John's 4
 
0.1%
Other values (4854) 4906
98.1%

Length

2023-01-27T10:27:49.189478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
la 388
 
3.0%
restaurant 340
 
2.6%
283
 
2.2%
bar 283
 
2.2%
the 217
 
1.7%
cafe 202
 
1.6%
le 190
 
1.5%
de 150
 
1.2%
pizzeria 145
 
1.1%
ristorante 130
 
1.0%
Other values (6160) 10606
82.0%

Most occurring characters

ValueCountFrequency (%)
a 8994
 
11.0%
7947
 
9.7%
e 7461
 
9.1%
r 5231
 
6.4%
i 5005
 
6.1%
o 4453
 
5.5%
t 4211
 
5.2%
n 4157
 
5.1%
s 3507
 
4.3%
l 2982
 
3.7%
Other values (156) 27684
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 59991
73.5%
Uppercase Letter 12512
 
15.3%
Space Separator 7947
 
9.7%
Other Punctuation 692
 
0.8%
Decimal Number 265
 
0.3%
Dash Punctuation 189
 
0.2%
Final Punctuation 15
 
< 0.1%
Modifier Symbol 5
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Other values (5) 10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8994
15.0%
e 7461
12.4%
r 5231
8.7%
i 5005
8.3%
o 4453
 
7.4%
t 4211
 
7.0%
n 4157
 
6.9%
s 3507
 
5.8%
l 2982
 
5.0%
u 2360
 
3.9%
Other values (75) 11630
19.4%
Uppercase Letter
ValueCountFrequency (%)
C 1252
 
10.0%
B 1104
 
8.8%
R 1031
 
8.2%
L 1023
 
8.2%
P 991
 
7.9%
S 789
 
6.3%
T 782
 
6.2%
A 743
 
5.9%
M 671
 
5.4%
D 567
 
4.5%
Other values (32) 3559
28.4%
Other Punctuation
ValueCountFrequency (%)
' 384
55.5%
& 196
28.3%
. 53
 
7.7%
, 21
 
3.0%
" 14
 
2.0%
/ 7
 
1.0%
! 6
 
0.9%
@ 4
 
0.6%
# 2
 
0.3%
: 2
 
0.3%
Other values (3) 3
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 54
20.4%
2 51
19.2%
0 32
12.1%
7 25
9.4%
3 24
9.1%
6 18
 
6.8%
4 18
 
6.8%
5 16
 
6.0%
9 15
 
5.7%
8 12
 
4.5%
Final Punctuation
ValueCountFrequency (%)
14
93.3%
1
 
6.7%
Modifier Symbol
ValueCountFrequency (%)
` 4
80.0%
´ 1
 
20.0%
Math Symbol
ValueCountFrequency (%)
~ 2
66.7%
+ 1
33.3%
Initial Punctuation
ValueCountFrequency (%)
2
66.7%
1
33.3%
Other Symbol
ValueCountFrequency (%)
° 1
50.0%
® 1
50.0%
Space Separator
ValueCountFrequency (%)
7947
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 189
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Nonspacing Mark
ValueCountFrequency (%)
́ 1
100.0%
Currency Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 72452
88.8%
Common 9128
 
11.2%
Greek 32
 
< 0.1%
Cyrillic 19
 
< 0.1%
Inherited 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8994
 
12.4%
e 7461
 
10.3%
r 5231
 
7.2%
i 5005
 
6.9%
o 4453
 
6.1%
t 4211
 
5.8%
n 4157
 
5.7%
s 3507
 
4.8%
l 2982
 
4.1%
u 2360
 
3.3%
Other values (87) 24091
33.3%
Common
ValueCountFrequency (%)
7947
87.1%
' 384
 
4.2%
& 196
 
2.1%
- 189
 
2.1%
1 54
 
0.6%
. 53
 
0.6%
2 51
 
0.6%
0 32
 
0.4%
7 25
 
0.3%
3 24
 
0.3%
Other values (28) 173
 
1.9%
Greek
ValueCountFrequency (%)
α 6
18.8%
ι 3
 
9.4%
τ 2
 
6.2%
ς 2
 
6.2%
ε 2
 
6.2%
ω 2
 
6.2%
ν 2
 
6.2%
ο 2
 
6.2%
χ 1
 
3.1%
Β 1
 
3.1%
Other values (9) 9
28.1%
Cyrillic
ValueCountFrequency (%)
а 3
15.8%
т 3
15.8%
е 2
10.5%
н 2
10.5%
с 2
10.5%
р 2
10.5%
и 1
 
5.3%
Н 1
 
5.3%
Р 1
 
5.3%
о 1
 
5.3%
Inherited
ValueCountFrequency (%)
́ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81243
99.5%
None 350
 
0.4%
Cyrillic 19
 
< 0.1%
Punctuation 18
 
< 0.1%
Diacriticals 1
 
< 0.1%
Currency Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8994
 
11.1%
7947
 
9.8%
e 7461
 
9.2%
r 5231
 
6.4%
i 5005
 
6.2%
o 4453
 
5.5%
t 4211
 
5.2%
n 4157
 
5.1%
s 3507
 
4.3%
l 2982
 
3.7%
Other values (71) 27295
33.6%
None
ValueCountFrequency (%)
é 87
24.9%
í 36
 
10.3%
è 24
 
6.9%
ü 20
 
5.7%
ö 20
 
5.7%
ó 18
 
5.1%
á 16
 
4.6%
ä 12
 
3.4%
ñ 9
 
2.6%
à 8
 
2.3%
Other values (58) 100
28.6%
Punctuation
ValueCountFrequency (%)
14
77.8%
2
 
11.1%
1
 
5.6%
1
 
5.6%
Cyrillic
ValueCountFrequency (%)
а 3
15.8%
т 3
15.8%
е 2
10.5%
н 2
10.5%
с 2
10.5%
р 2
10.5%
и 1
 
5.3%
Н 1
 
5.3%
Р 1
 
5.3%
о 1
 
5.3%
Diacriticals
ValueCountFrequency (%)
́ 1
100.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%
Distinct3157
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
["Europe", "United Kingdom (UK)", "England", "London"]
 
91
["Europe", "France", "Ile-de-France", "Paris"]
 
69
["Europe", "Spain", "Community of Madrid", "Madrid"]
 
55
["Europe", "Italy", "Lazio", "Rome"]
 
52
["Europe", "Czech Republic", "Bohemia", "Prague"]
 
41
Other values (3152)
4692 

Length

Max length140
Median length104
Mean length66.7376
Min length28

Characters and Unicode

Total characters333688
Distinct characters64
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2541 ?
Unique (%)50.8%

Sample

1st row["Europe", "United Kingdom (UK)", "England", "West Midlands", "Birmingham"]
2nd row["Europe", "Spain", "Valencian Country", "Province of Alicante", "Costa Blanca", "L'Alfas del Pi", "El Albir"]
3rd row["Europe", "Greece", "Crete", "Heraklion Prefecture", "Kaloi Limenes"]
4th row["Europe", "Greece", "Central Macedonia", "Thessaloniki Region", "Nea Madytos"]
5th row["Europe", "Italy", "Sicily", "Province of Enna", "Valguarnera Caropepe"]

Common Values

ValueCountFrequency (%)
["Europe", "United Kingdom (UK)", "England", "London"] 91
 
1.8%
["Europe", "France", "Ile-de-France", "Paris"] 69
 
1.4%
["Europe", "Spain", "Community of Madrid", "Madrid"] 55
 
1.1%
["Europe", "Italy", "Lazio", "Rome"] 52
 
1.0%
["Europe", "Czech Republic", "Bohemia", "Prague"] 41
 
0.8%
["Europe", "Italy", "Lombardy", "Milan"] 40
 
0.8%
["Europe", "Spain", "Catalonia", "Province of Barcelona", "Barcelona"] 37
 
0.7%
["Europe", "Germany", "Berlin"] 28
 
0.6%
["Europe", "Austria", "Vienna Region", "Vienna"] 26
 
0.5%
["Europe", "Italy", "Campania", "Province of Naples", "Naples"] 22
 
0.4%
Other values (3147) 4539
90.8%

Length

2023-01-27T10:27:49.326345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
europe 5000
 
14.7%
province 1820
 
5.3%
of 1566
 
4.6%
italy 1038
 
3.0%
kingdom 774
 
2.3%
uk 774
 
2.3%
united 774
 
2.3%
france 756
 
2.2%
spain 708
 
2.1%
england 647
 
1.9%
Other values (4155) 20202
59.3%

Most occurring characters

ValueCountFrequency (%)
" 48514
14.5%
29059
 
8.7%
e 23742
 
7.1%
a 19804
 
5.9%
, 19257
 
5.8%
r 18556
 
5.6%
o 18441
 
5.5%
n 17517
 
5.2%
i 13667
 
4.1%
u 9577
 
2.9%
Other values (54) 115554
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 188375
56.5%
Other Punctuation 68022
 
20.4%
Uppercase Letter 34423
 
10.3%
Space Separator 29059
 
8.7%
Close Punctuation 5780
 
1.7%
Open Punctuation 5780
 
1.7%
Dash Punctuation 2245
 
0.7%
Decimal Number 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 23742
12.6%
a 19804
10.5%
r 18556
9.9%
o 18441
9.8%
n 17517
9.3%
i 13667
 
7.3%
u 9577
 
5.1%
l 9397
 
5.0%
t 8793
 
4.7%
d 6931
 
3.7%
Other values (16) 41950
22.3%
Uppercase Letter
ValueCountFrequency (%)
E 6058
17.6%
P 3493
 
10.1%
C 2433
 
7.1%
S 2392
 
6.9%
A 1869
 
5.4%
K 1762
 
5.1%
U 1722
 
5.0%
I 1568
 
4.6%
B 1545
 
4.5%
F 1530
 
4.4%
Other values (16) 10051
29.2%
Other Punctuation
ValueCountFrequency (%)
" 48514
71.3%
, 19257
 
28.3%
' 247
 
0.4%
. 4
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
] 5000
86.5%
) 780
 
13.5%
Open Punctuation
ValueCountFrequency (%)
[ 5000
86.5%
( 780
 
13.5%
Decimal Number
ValueCountFrequency (%)
0 3
75.0%
2 1
 
25.0%
Space Separator
ValueCountFrequency (%)
29059
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2245
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 222798
66.8%
Common 110890
33.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 23742
 
10.7%
a 19804
 
8.9%
r 18556
 
8.3%
o 18441
 
8.3%
n 17517
 
7.9%
i 13667
 
6.1%
u 9577
 
4.3%
l 9397
 
4.2%
t 8793
 
3.9%
d 6931
 
3.1%
Other values (42) 76373
34.3%
Common
ValueCountFrequency (%)
" 48514
43.7%
29059
26.2%
, 19257
 
17.4%
] 5000
 
4.5%
[ 5000
 
4.5%
- 2245
 
2.0%
( 780
 
0.7%
) 780
 
0.7%
' 247
 
0.2%
. 4
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 333688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
" 48514
14.5%
29059
 
8.7%
e 23742
 
7.1%
a 19804
 
5.9%
, 19257
 
5.8%
r 18556
 
5.6%
o 18441
 
5.5%
n 17517
 
5.2%
i 13667
 
4.1%
u 9577
 
2.9%
Other values (54) 115554
34.6%

country
Categorical

Distinct24
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Italy
1038 
France
756 
Spain
708 
England
647 
Germany
506 
Other values (19)
1345 

Length

Max length16
Median length15
Mean length6.4804
Min length5

Characters and Unicode

Total characters32402
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEngland
2nd rowSpain
3rd rowGreece
4th rowGreece
5th rowItaly

Common Values

ValueCountFrequency (%)
Italy 1038
20.8%
France 756
15.1%
Spain 708
14.2%
England 647
12.9%
Germany 506
10.1%
The Netherlands 145
 
2.9%
Greece 144
 
2.9%
Belgium 138
 
2.8%
Poland 129
 
2.6%
Portugal 126
 
2.5%
Other values (14) 663
13.3%

Length

2023-01-27T10:27:49.440368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
italy 1038
19.8%
france 756
14.4%
spain 708
13.5%
england 647
12.3%
germany 506
9.7%
the 145
 
2.8%
netherlands 145
 
2.8%
greece 144
 
2.7%
belgium 138
 
2.6%
poland 129
 
2.5%
Other values (16) 884
16.9%

Most occurring characters

ValueCountFrequency (%)
a 4659
14.4%
n 3957
12.2%
e 2766
 
8.5%
l 2568
 
7.9%
r 2015
 
6.2%
y 1576
 
4.9%
t 1524
 
4.7%
d 1182
 
3.6%
i 1162
 
3.6%
c 1132
 
3.5%
Other values (28) 9861
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26922
83.1%
Uppercase Letter 5240
 
16.2%
Space Separator 240
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4659
17.3%
n 3957
14.7%
e 2766
10.3%
l 2568
9.5%
r 2015
7.5%
y 1576
 
5.9%
t 1524
 
5.7%
d 1182
 
4.4%
i 1162
 
4.3%
c 1132
 
4.2%
Other values (12) 4381
16.3%
Uppercase Letter
ValueCountFrequency (%)
I 1116
21.3%
S 871
16.6%
F 791
15.1%
G 650
12.4%
E 647
12.3%
P 255
 
4.9%
B 161
 
3.1%
N 159
 
3.0%
T 145
 
2.8%
C 112
 
2.1%
Other values (5) 333
 
6.4%
Space Separator
ValueCountFrequency (%)
240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32162
99.3%
Common 240
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4659
14.5%
n 3957
12.3%
e 2766
 
8.6%
l 2568
 
8.0%
r 2015
 
6.3%
y 1576
 
4.9%
t 1524
 
4.7%
d 1182
 
3.7%
i 1162
 
3.6%
c 1132
 
3.5%
Other values (27) 9621
29.9%
Common
ValueCountFrequency (%)
240
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32402
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4659
14.4%
n 3957
12.2%
e 2766
 
8.5%
l 2568
 
7.9%
r 2015
 
6.2%
y 1576
 
4.9%
t 1524
 
4.7%
d 1182
 
3.6%
i 1162
 
3.6%
c 1132
 
3.5%
Other values (28) 9861
30.4%

region
Categorical

HIGH CARDINALITY  MISSING 

Distinct218
Distinct (%)4.6%
Missing227
Missing (%)4.5%
Memory size78.1 KiB
Lombardy
 
146
Andalucia
 
138
Ile-de-France
 
129
Occitanie
 
111
Provence-Alpes-Cote d'Azur
 
107
Other values (213)
4142 

Length

Max length26
Median length22
Mean length11.514352
Min length4

Characters and Unicode

Total characters54958
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.5%

Sample

1st rowWest Midlands
2nd rowValencian Country
3rd rowCrete
4th rowCentral Macedonia
5th rowSicily

Common Values

ValueCountFrequency (%)
Lombardy 146
 
2.9%
Andalucia 138
 
2.8%
Ile-de-France 129
 
2.6%
Occitanie 111
 
2.2%
Provence-Alpes-Cote d'Azur 107
 
2.1%
Catalonia 103
 
2.1%
Auvergne-Rhone-Alpes 103
 
2.1%
Lazio 100
 
2.0%
Bavaria 95
 
1.9%
Campania 91
 
1.8%
Other values (208) 3650
73.0%
(Missing) 227
 
4.5%

Length

2023-01-27T10:27:49.543041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 173
 
2.6%
central 156
 
2.3%
lombardy 146
 
2.2%
andalucia 138
 
2.1%
poland 129
 
1.9%
ile-de-france 129
 
1.9%
london 125
 
1.9%
islands 117
 
1.8%
occitanie 111
 
1.7%
provence-alpes-cote 107
 
1.6%
Other values (234) 5312
80.0%

Most occurring characters

ValueCountFrequency (%)
a 5838
 
10.6%
e 5109
 
9.3%
n 4316
 
7.9%
r 3699
 
6.7%
i 3573
 
6.5%
o 3238
 
5.9%
t 2711
 
4.9%
l 2561
 
4.7%
s 2164
 
3.9%
d 1938
 
3.5%
Other values (43) 19811
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44388
80.8%
Uppercase Letter 7378
 
13.4%
Space Separator 1870
 
3.4%
Dash Punctuation 1208
 
2.2%
Other Punctuation 114
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5838
13.2%
e 5109
11.5%
n 4316
9.7%
r 3699
8.3%
i 3573
 
8.0%
o 3238
 
7.3%
t 2711
 
6.1%
l 2561
 
5.8%
s 2164
 
4.9%
d 1938
 
4.4%
Other values (16) 9241
20.8%
Uppercase Letter
ValueCountFrequency (%)
A 1001
13.6%
C 972
13.2%
L 652
 
8.8%
P 626
 
8.5%
B 474
 
6.4%
S 451
 
6.1%
R 347
 
4.7%
W 322
 
4.4%
F 303
 
4.1%
M 301
 
4.1%
Other values (14) 1929
26.1%
Space Separator
ValueCountFrequency (%)
1870
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1208
100.0%
Other Punctuation
ValueCountFrequency (%)
' 114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 51766
94.2%
Common 3192
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5838
 
11.3%
e 5109
 
9.9%
n 4316
 
8.3%
r 3699
 
7.1%
i 3573
 
6.9%
o 3238
 
6.3%
t 2711
 
5.2%
l 2561
 
4.9%
s 2164
 
4.2%
d 1938
 
3.7%
Other values (40) 16619
32.1%
Common
ValueCountFrequency (%)
1870
58.6%
- 1208
37.8%
' 114
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54958
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5838
 
10.6%
e 5109
 
9.3%
n 4316
 
7.9%
r 3699
 
6.7%
i 3573
 
6.5%
o 3238
 
5.9%
t 2711
 
4.9%
l 2561
 
4.7%
s 2164
 
3.9%
d 1938
 
3.5%
Other values (43) 19811
36.0%

province
Categorical

HIGH CARDINALITY  MISSING 

Distinct536
Distinct (%)15.5%
Missing1546
Missing (%)30.9%
Memory size78.1 KiB
Province of Barcelona
 
69
Province of Malaga
 
52
French Riviera - Cote d'Azur
 
49
Province of Turin
 
42
Upper Bavaria
 
38
Other values (531)
3204 

Length

Max length43
Median length27
Mean length15.892588
Min length3

Characters and Unicode

Total characters54893
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145 ?
Unique (%)4.2%

Sample

1st rowProvince of Alicante
2nd rowHeraklion Prefecture
3rd rowThessaloniki Region
4th rowProvince of Enna
5th rowCote d'Or

Common Values

ValueCountFrequency (%)
Province of Barcelona 69
 
1.4%
Province of Malaga 52
 
1.0%
French Riviera - Cote d'Azur 49
 
1.0%
Province of Turin 42
 
0.8%
Upper Bavaria 38
 
0.8%
Province of Naples 38
 
0.8%
Province of Valencia 36
 
0.7%
Province of Alicante 35
 
0.7%
North Holland Province 35
 
0.7%
Italian Riviera 32
 
0.6%
Other values (526) 3028
60.6%
(Missing) 1546
30.9%

Length

2023-01-27T10:27:49.655713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
province 1759
22.1%
of 1380
 
17.4%
county 219
 
2.8%
district 130
 
1.6%
region 110
 
1.4%
riviera 83
 
1.0%
south 78
 
1.0%
north 72
 
0.9%
barcelona 69
 
0.9%
holland 60
 
0.8%
Other values (583) 3991
50.2%

Most occurring characters

ValueCountFrequency (%)
o 5599
 
10.2%
e 5093
 
9.3%
4497
 
8.2%
r 4374
 
8.0%
i 4245
 
7.7%
n 4181
 
7.6%
a 3903
 
7.1%
c 2526
 
4.6%
v 2164
 
3.9%
P 2060
 
3.8%
Other values (44) 16251
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42955
78.3%
Uppercase Letter 6839
 
12.5%
Space Separator 4497
 
8.2%
Dash Punctuation 529
 
1.0%
Other Punctuation 73
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 5599
13.0%
e 5093
11.9%
r 4374
10.2%
i 4245
9.9%
n 4181
9.7%
a 3903
9.1%
c 2526
 
5.9%
v 2164
 
5.0%
t 1771
 
4.1%
l 1524
 
3.5%
Other values (16) 7575
17.6%
Uppercase Letter
ValueCountFrequency (%)
P 2060
30.1%
C 619
 
9.1%
S 391
 
5.7%
B 354
 
5.2%
R 328
 
4.8%
A 325
 
4.8%
M 317
 
4.6%
L 296
 
4.3%
V 246
 
3.6%
H 242
 
3.5%
Other values (15) 1661
24.3%
Space Separator
ValueCountFrequency (%)
4497
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 529
100.0%
Other Punctuation
ValueCountFrequency (%)
' 73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49794
90.7%
Common 5099
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 5599
11.2%
e 5093
 
10.2%
r 4374
 
8.8%
i 4245
 
8.5%
n 4181
 
8.4%
a 3903
 
7.8%
c 2526
 
5.1%
v 2164
 
4.3%
P 2060
 
4.1%
t 1771
 
3.6%
Other values (41) 13878
27.9%
Common
ValueCountFrequency (%)
4497
88.2%
- 529
 
10.4%
' 73
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 5599
 
10.2%
e 5093
 
9.3%
4497
 
8.2%
r 4374
 
8.0%
i 4245
 
7.7%
n 4181
 
7.6%
a 3903
 
7.1%
c 2526
 
4.6%
v 2164
 
3.9%
P 2060
 
3.8%
Other values (44) 16251
29.6%

city
Categorical

HIGH CARDINALITY  MISSING 

Distinct2004
Distinct (%)64.0%
Missing1871
Missing (%)37.4%
Memory size78.1 KiB
Paris
 
69
Madrid
 
55
Rome
 
52
Prague
 
41
Milan
 
40
Other values (1999)
2872 

Length

Max length40
Median length28
Mean length9.1300735
Min length3

Characters and Unicode

Total characters28568
Distinct characters60
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1612 ?
Unique (%)51.5%

Sample

1st rowBirmingham
2nd rowKaloi Limenes
3rd rowNea Madytos
4th rowNottingham
5th rowDijon

Common Values

ValueCountFrequency (%)
Paris 69
 
1.4%
Madrid 55
 
1.1%
Rome 52
 
1.0%
Prague 41
 
0.8%
Milan 40
 
0.8%
Vienna 26
 
0.5%
Amsterdam 21
 
0.4%
Dublin 18
 
0.4%
Lisbon 18
 
0.4%
Munich 15
 
0.3%
Other values (1994) 2774
55.5%
(Missing) 1871
37.4%

Length

2023-01-27T10:27:49.780886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 75
 
2.0%
paris 69
 
1.8%
madrid 55
 
1.5%
rome 52
 
1.4%
prague 41
 
1.1%
milan 40
 
1.1%
la 35
 
0.9%
vienna 26
 
0.7%
le 22
 
0.6%
amsterdam 21
 
0.6%
Other values (2188) 3349
88.5%

Most occurring characters

ValueCountFrequency (%)
e 3066
 
10.7%
a 2584
 
9.0%
r 2103
 
7.4%
n 2031
 
7.1%
i 1755
 
6.1%
o 1676
 
5.9%
l 1460
 
5.1%
s 1362
 
4.8%
t 1180
 
4.1%
u 966
 
3.4%
Other values (50) 10385
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23498
82.3%
Uppercase Letter 3931
 
13.8%
Space Separator 656
 
2.3%
Dash Punctuation 443
 
1.6%
Other Punctuation 26
 
0.1%
Open Punctuation 5
 
< 0.1%
Close Punctuation 5
 
< 0.1%
Decimal Number 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3066
13.0%
a 2584
11.0%
r 2103
 
8.9%
n 2031
 
8.6%
i 1755
 
7.5%
o 1676
 
7.1%
l 1460
 
6.2%
s 1362
 
5.8%
t 1180
 
5.0%
u 966
 
4.1%
Other values (16) 5315
22.6%
Uppercase Letter
ValueCountFrequency (%)
M 380
 
9.7%
S 376
 
9.6%
P 341
 
8.7%
B 321
 
8.2%
C 320
 
8.1%
L 296
 
7.5%
A 241
 
6.1%
H 181
 
4.6%
R 164
 
4.2%
N 145
 
3.7%
Other values (16) 1166
29.7%
Other Punctuation
ValueCountFrequency (%)
' 22
84.6%
. 4
 
15.4%
Decimal Number
ValueCountFrequency (%)
0 3
75.0%
2 1
 
25.0%
Space Separator
ValueCountFrequency (%)
656
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 443
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27429
96.0%
Common 1139
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3066
 
11.2%
a 2584
 
9.4%
r 2103
 
7.7%
n 2031
 
7.4%
i 1755
 
6.4%
o 1676
 
6.1%
l 1460
 
5.3%
s 1362
 
5.0%
t 1180
 
4.3%
u 966
 
3.5%
Other values (42) 9246
33.7%
Common
ValueCountFrequency (%)
656
57.6%
- 443
38.9%
' 22
 
1.9%
( 5
 
0.4%
) 5
 
0.4%
. 4
 
0.4%
0 3
 
0.3%
2 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3066
 
10.7%
a 2584
 
9.0%
r 2103
 
7.4%
n 2031
 
7.1%
i 1755
 
6.1%
o 1676
 
5.9%
l 1460
 
5.1%
s 1362
 
4.8%
t 1180
 
4.1%
u 966
 
3.4%
Other values (50) 10385
36.4%

address
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct4999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Opatovická 3, Prague 11000 Czech Republic
 
2
The Oasis 110-114 Corporation Street Oasis Market, Birmingham B4 6SX England
 
1
Puerto Deportivo Marina Botafoch, Local 106, 07800, Ibiza Spain
 
1
92 avenue du Gendarme Castermant, 77500 Chelles France
 
1
CC La Minilla, Las Palmas de Gran Canaria, Gran Canaria Spain
 
1
Other values (4994)
4994 

Length

Max length151
Median length111
Mean length49.6714
Min length12

Characters and Unicode

Total characters248357
Distinct characters140
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4998 ?
Unique (%)> 99.9%

Sample

1st rowThe Oasis 110-114 Corporation Street Oasis Market, Birmingham B4 6SX England
2nd rowCarrer Vivaldi, 11 Local 2, 03581 El Albir, L'Alfas del Pi Spain
3rd rowKaloi Limenes, Crete 704 00 Greece
4th rowNea Madytos 57014 Greece
5th rowVia Giuseppe Mazzini 122, 94019 Valguarnera Caropepe, Sicily Italy

Common Values

ValueCountFrequency (%)
Opatovická 3, Prague 11000 Czech Republic 2
 
< 0.1%
The Oasis 110-114 Corporation Street Oasis Market, Birmingham B4 6SX England 1
 
< 0.1%
Puerto Deportivo Marina Botafoch, Local 106, 07800, Ibiza Spain 1
 
< 0.1%
92 avenue du Gendarme Castermant, 77500 Chelles France 1
 
< 0.1%
CC La Minilla, Las Palmas de Gran Canaria, Gran Canaria Spain 1
 
< 0.1%
Castlebrook Compton Dundon, Somerton TA11 6PR England 1
 
< 0.1%
34 Place Notre Dame du Mont Cours Julien, 13006 Marseille France 1
 
< 0.1%
M6 Moto serrvices Carnforth, Burton-in-Kendal LA6 1JF England 1
 
< 0.1%
Cowbridge Road, Llantwit Major CF61 2YS Wales 1
 
< 0.1%
Harbourside Explore Lane, Bristol BS1 5TY England 1
 
< 0.1%
Other values (4989) 4989
99.8%

Length

2023-01-27T10:27:49.909368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
italy 1039
 
2.7%
france 763
 
2.0%
de 736
 
1.9%
spain 710
 
1.9%
via 691
 
1.8%
england 647
 
1.7%
germany 506
 
1.3%
rue 359
 
0.9%
calle 305
 
0.8%
1 292
 
0.8%
Other values (14579) 31991
84.1%

Most occurring characters

ValueCountFrequency (%)
33042
 
13.3%
a 20898
 
8.4%
e 18471
 
7.4%
n 12770
 
5.1%
r 12516
 
5.0%
i 10752
 
4.3%
l 10374
 
4.2%
o 9708
 
3.9%
t 8715
 
3.5%
, 7040
 
2.8%
Other values (130) 104071
41.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 146150
58.8%
Space Separator 33042
 
13.3%
Decimal Number 30498
 
12.3%
Uppercase Letter 29320
 
11.8%
Other Punctuation 8072
 
3.3%
Dash Punctuation 1245
 
0.5%
Close Punctuation 8
 
< 0.1%
Other Letter 7
 
< 0.1%
Open Punctuation 7
 
< 0.1%
Other Symbol 5
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 20898
14.3%
e 18471
12.6%
n 12770
8.7%
r 12516
8.6%
i 10752
 
7.4%
l 10374
 
7.1%
o 9708
 
6.6%
t 8715
 
6.0%
s 6057
 
4.1%
d 5770
 
3.9%
Other values (66) 30119
20.6%
Uppercase Letter
ValueCountFrequency (%)
S 3370
 
11.5%
C 2360
 
8.0%
P 2101
 
7.2%
B 1849
 
6.3%
A 1680
 
5.7%
G 1642
 
5.6%
M 1531
 
5.2%
R 1491
 
5.1%
L 1443
 
4.9%
I 1420
 
4.8%
Other values (26) 10433
35.6%
Decimal Number
ValueCountFrequency (%)
0 6110
20.0%
1 5209
17.1%
2 3512
11.5%
3 2972
9.7%
4 2594
8.5%
5 2405
 
7.9%
6 2129
 
7.0%
8 2010
 
6.6%
7 1927
 
6.3%
9 1630
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 7040
87.2%
. 589
 
7.3%
/ 262
 
3.2%
' 156
 
1.9%
& 14
 
0.2%
" 6
 
0.1%
: 2
 
< 0.1%
# 2
 
< 0.1%
; 1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
° 4
80.0%
1
 
20.0%
Space Separator
ValueCountFrequency (%)
33042
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1245
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Other Letter
ValueCountFrequency (%)
º 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
| 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 175449
70.6%
Common 72880
29.3%
Greek 28
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 20898
 
11.9%
e 18471
 
10.5%
n 12770
 
7.3%
r 12516
 
7.1%
i 10752
 
6.1%
l 10374
 
5.9%
o 9708
 
5.5%
t 8715
 
5.0%
s 6057
 
3.5%
d 5770
 
3.3%
Other values (87) 59418
33.9%
Common
ValueCountFrequency (%)
33042
45.3%
, 7040
 
9.7%
0 6110
 
8.4%
1 5209
 
7.1%
2 3512
 
4.8%
3 2972
 
4.1%
4 2594
 
3.6%
5 2405
 
3.3%
6 2129
 
2.9%
8 2010
 
2.8%
Other values (17) 5857
 
8.0%
Greek
ValueCountFrequency (%)
α 5
17.9%
ε 4
14.3%
ί 2
 
7.1%
τ 2
 
7.1%
θ 2
 
7.1%
λ 2
 
7.1%
υ 2
 
7.1%
Π 1
 
3.6%
σ 1
 
3.6%
ρ 1
 
3.6%
Other values (6) 6
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 247915
99.8%
None 439
 
0.2%
Punctuation 2
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33042
 
13.3%
a 20898
 
8.4%
e 18471
 
7.5%
n 12770
 
5.2%
r 12516
 
5.0%
i 10752
 
4.3%
l 10374
 
4.2%
o 9708
 
3.9%
t 8715
 
3.5%
, 7040
 
2.8%
Other values (66) 103629
41.8%
None
ValueCountFrequency (%)
ü 89
20.3%
é 48
 
10.9%
á 29
 
6.6%
ó 27
 
6.2%
ß 23
 
5.2%
ö 17
 
3.9%
ä 16
 
3.6%
à 14
 
3.2%
í 13
 
3.0%
ł 12
 
2.7%
Other values (52) 151
34.4%
Punctuation
ValueCountFrequency (%)
2
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

latitude
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4918
Distinct (%)99.7%
Missing69
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean46.645764
Minimum27.75227
Maximum66.50124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:50.034665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum27.75227
5-th percentile37.378613
Q142.320567
median46.771465
Q351.409289
95-th percentile54.8577
Maximum66.50124
Range38.74897
Interquartile range (IQR)9.0887215

Descriptive statistics

Standard deviation5.8191201
Coefficient of variation (CV)0.12475131
Kurtosis-0.043649102
Mean46.645764
Median Absolute Deviation (MAD)4.612715
Skewness-0.25110938
Sum230010.26
Variance33.862158
MonotonicityNot monotonic
2023-01-27T10:27:50.148661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.003227 3
 
0.1%
51.12472 2
 
< 0.1%
45.419262 2
 
< 0.1%
45.5939 2
 
< 0.1%
50.64471 2
 
< 0.1%
45.60455 2
 
< 0.1%
28.86337 2
 
< 0.1%
40.81514 2
 
< 0.1%
50.81556 2
 
< 0.1%
46.62773 2
 
< 0.1%
Other values (4908) 4910
98.2%
(Missing) 69
 
1.4%
ValueCountFrequency (%)
27.75227 1
< 0.1%
27.761194 1
< 0.1%
27.76695 1
< 0.1%
27.7706 1
< 0.1%
27.791706 1
< 0.1%
27.901413 1
< 0.1%
27.990969 1
< 0.1%
28.00207 1
< 0.1%
28.005135 1
< 0.1%
28.023865 1
< 0.1%
ValueCountFrequency (%)
66.50124 1
< 0.1%
65.85564 1
< 0.1%
65.85512 1
< 0.1%
65.20793 1
< 0.1%
63.826847 1
< 0.1%
63.400135 1
< 0.1%
63.17525 1
< 0.1%
62.892227 1
< 0.1%
62.189133 1
< 0.1%
61.739826 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4926
Distinct (%)99.9%
Missing69
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean5.8175102
Minimum-31.16808
Maximum29.586159
Zeros0
Zeros (%)0.0%
Negative1536
Negative (%)30.7%
Memory size78.1 KiB
2023-01-27T10:27:50.266561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-31.16808
5-th percentile-7.3875525
Q1-0.8770415
median5.71413
Q312.077963
95-th percentile20.953077
Maximum29.586159
Range60.754239
Interquartile range (IQR)12.955005

Descriptive statistics

Standard deviation8.6370924
Coefficient of variation (CV)1.4846716
Kurtosis-0.11860022
Mean5.8175102
Median Absolute Deviation (MAD)6.478196
Skewness0.083484179
Sum28686.143
Variance74.599366
MonotonicityNot monotonic
2023-01-27T10:27:50.373599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.083693 3
 
0.1%
-2.74 2
 
< 0.1%
-13.82904 2
 
< 0.1%
14.30984 2
 
< 0.1%
-0.0234 1
 
< 0.1%
-2.729453 1
 
< 0.1%
5.384831 1
 
< 0.1%
-2.733867 1
 
< 0.1%
-3.48853 1
 
< 0.1%
-2.601097 1
 
< 0.1%
Other values (4916) 4916
98.3%
(Missing) 69
 
1.4%
ValueCountFrequency (%)
-31.16808 1
< 0.1%
-28.536182 1
< 0.1%
-28.533255 1
< 0.1%
-28.528028 1
< 0.1%
-25.668888 1
< 0.1%
-25.66241 1
< 0.1%
-25.429443 1
< 0.1%
-17.98107 1
< 0.1%
-17.95514 1
< 0.1%
-16.931684 1
< 0.1%
ValueCountFrequency (%)
29.586159 1
< 0.1%
28.95707 1
< 0.1%
28.652384 1
< 0.1%
28.232141 1
< 0.1%
28.229578 1
< 0.1%
28.224382 1
< 0.1%
28.22166 1
< 0.1%
28.17577 1
< 0.1%
28.163492 1
< 0.1%
28.14094 1
< 0.1%

claimed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing7
Missing (%)0.1%
Memory size78.1 KiB
Unclaimed
2776 
Claimed
2217 

Length

Max length9
Median length9
Mean length8.1119567
Min length7

Characters and Unicode

Total characters40503
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnclaimed
2nd rowClaimed
3rd rowUnclaimed
4th rowClaimed
5th rowUnclaimed

Common Values

ValueCountFrequency (%)
Unclaimed 2776
55.5%
Claimed 2217
44.3%
(Missing) 7
 
0.1%

Length

2023-01-27T10:27:50.475877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T10:27:50.584823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
unclaimed 2776
55.6%
claimed 2217
44.4%

Most occurring characters

ValueCountFrequency (%)
l 4993
12.3%
a 4993
12.3%
i 4993
12.3%
m 4993
12.3%
e 4993
12.3%
d 4993
12.3%
U 2776
6.9%
n 2776
6.9%
c 2776
6.9%
C 2217
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35510
87.7%
Uppercase Letter 4993
 
12.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 4993
14.1%
a 4993
14.1%
i 4993
14.1%
m 4993
14.1%
e 4993
14.1%
d 4993
14.1%
n 2776
7.8%
c 2776
7.8%
Uppercase Letter
ValueCountFrequency (%)
U 2776
55.6%
C 2217
44.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 40503
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 4993
12.3%
a 4993
12.3%
i 4993
12.3%
m 4993
12.3%
e 4993
12.3%
d 4993
12.3%
U 2776
6.9%
n 2776
6.9%
c 2776
6.9%
C 2217
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40503
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 4993
12.3%
a 4993
12.3%
i 4993
12.3%
m 4993
12.3%
e 4993
12.3%
d 4993
12.3%
U 2776
6.9%
n 2776
6.9%
c 2776
6.9%
C 2217
5.5%

awards
Categorical

HIGH CARDINALITY  MISSING 

Distinct176
Distinct (%)14.4%
Missing3774
Missing (%)75.5%
Memory size78.1 KiB
Travellers' Choice, Certificate of Excellence 2020
109 
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019
 
79
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017, Certificate of Excellence 2016
 
73
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017
 
70
Certificate of Excellence 2017
 
67
Other values (171)
828 

Length

Max length338
Median length254
Mean length112.177
Min length30

Characters and Unicode

Total characters137529
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)7.3%

Sample

1st rowTravellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018
2nd rowTravellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018
3rd rowCertificate of Excellence 2018, Certificate of Excellence 2016, Certificate of Excellence 2015
4th rowTravellers' Choice, Certificate of Excellence 2020
5th rowTravellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017, Certificate of Excellence 2016

Common Values

ValueCountFrequency (%)
Travellers' Choice, Certificate of Excellence 2020 109
 
2.2%
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019 79
 
1.6%
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017, Certificate of Excellence 2016 73
 
1.5%
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017 70
 
1.4%
Certificate of Excellence 2017 67
 
1.3%
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018 50
 
1.0%
Certificate of Excellence 2019 48
 
1.0%
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017, Certificate of Excellence 2016, Certificate of Excellence 2015 47
 
0.9%
Certificate of Excellence 2018 41
 
0.8%
Certificate of Excellence 2018, Certificate of Excellence 2017 31
 
0.6%
Other values (166) 611
 
12.2%
(Missing) 3774
75.5%

Length

2023-01-27T10:27:50.686669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 3773
21.9%
certificate 3772
21.9%
excellence 3772
21.9%
travellers 695
 
4.0%
choice 695
 
4.0%
2020 677
 
3.9%
2019 653
 
3.8%
2017 651
 
3.8%
2018 627
 
3.6%
2016 494
 
2.9%
Other values (31) 1442
 
8.4%

Most occurring characters

ValueCountFrequency (%)
e 21316
15.5%
16025
11.7%
c 12233
 
8.9%
l 9205
 
6.7%
i 8659
 
6.3%
t 7779
 
5.7%
f 7687
 
5.6%
r 5452
 
4.0%
o 4876
 
3.5%
2 4699
 
3.4%
Other values (40) 39598
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 92553
67.3%
Space Separator 16025
 
11.7%
Decimal Number 15520
 
11.3%
Uppercase Letter 9304
 
6.8%
Other Punctuation 4127
 
3.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 21316
23.0%
c 12233
13.2%
l 9205
9.9%
i 8659
9.4%
t 7779
 
8.4%
f 7687
 
8.3%
r 5452
 
5.9%
o 4876
 
5.3%
a 4696
 
5.1%
n 4054
 
4.4%
Other values (14) 6596
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
C 4491
48.3%
E 3773
40.6%
T 733
 
7.9%
M 157
 
1.7%
G 42
 
0.5%
P 37
 
0.4%
S 32
 
0.3%
O 11
 
0.1%
H 11
 
0.1%
V 10
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 4699
30.3%
0 4557
29.4%
1 3224
20.8%
9 653
 
4.2%
7 651
 
4.2%
8 627
 
4.0%
6 494
 
3.2%
5 296
 
1.9%
4 170
 
1.1%
3 149
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 3366
81.6%
' 695
 
16.8%
: 54
 
1.3%
! 12
 
0.3%
Space Separator
ValueCountFrequency (%)
16025
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 101857
74.1%
Common 35672
 
25.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 21316
20.9%
c 12233
12.0%
l 9205
9.0%
i 8659
8.5%
t 7779
 
7.6%
f 7687
 
7.5%
r 5452
 
5.4%
o 4876
 
4.8%
a 4696
 
4.6%
C 4491
 
4.4%
Other values (25) 15463
15.2%
Common
ValueCountFrequency (%)
16025
44.9%
2 4699
 
13.2%
0 4557
 
12.8%
, 3366
 
9.4%
1 3224
 
9.0%
' 695
 
1.9%
9 653
 
1.8%
7 651
 
1.8%
8 627
 
1.8%
6 494
 
1.4%
Other values (5) 681
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 21316
15.5%
16025
11.7%
c 12233
 
8.9%
l 9205
 
6.7%
i 8659
 
6.3%
t 7779
 
5.7%
f 7687
 
5.6%
r 5452
 
4.0%
o 4876
 
3.5%
2 4699
 
3.4%
Other values (40) 39598
28.8%

popularity_detailed
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct4585
Distinct (%)100.0%
Missing415
Missing (%)8.3%
Memory size78.1 KiB
#45 of 99 Coffee & Tea in Birmingham
 
1
#1 of 1 Restaurant in Stepps
 
1
#508 of 929 Restaurants in Cordoba
 
1
#1 of 1 Coffee & Tea in Constantine Bay
 
1
#261 of 303 Restaurants in Ibiza
 
1
Other values (4580)
4580 

Length

Max length63
Median length53
Mean length34.859324
Min length21

Characters and Unicode

Total characters159830
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4585 ?
Unique (%)100.0%

Sample

1st row#45 of 99 Coffee & Tea in Birmingham
2nd row#18 of 128 Restaurants in El Albir
3rd row#2 of 2 Restaurants in Kaloi Limenes
4th row#3 of 3 Restaurants in Nea Madytos
5th row#9 of 10 Restaurants in Valguarnera Caropepe

Common Values

ValueCountFrequency (%)
#45 of 99 Coffee & Tea in Birmingham 1
 
< 0.1%
#1 of 1 Restaurant in Stepps 1
 
< 0.1%
#508 of 929 Restaurants in Cordoba 1
 
< 0.1%
#1 of 1 Coffee & Tea in Constantine Bay 1
 
< 0.1%
#261 of 303 Restaurants in Ibiza 1
 
< 0.1%
#5 of 7 Restaurants in Marina Palmense 1
 
< 0.1%
#5 of 44 Restaurants in Chelles 1
 
< 0.1%
#664 of 1246 Restaurants in Las Palmas de Gran Canaria 1
 
< 0.1%
#10 of 20 Restaurants in Somerton 1
 
< 0.1%
#616 of 2075 Restaurants in Marseille 1
 
< 0.1%
Other values (4575) 4575
91.5%
(Missing) 415
 
8.3%

Length

2023-01-27T10:27:50.794397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in 4595
 
15.7%
of 4587
 
15.7%
restaurants 4069
 
13.9%
1 531
 
1.8%
2 348
 
1.2%
3 332
 
1.1%
4 242
 
0.8%
5 193
 
0.7%
191
 
0.7%
coffee 186
 
0.6%
Other values (4545) 14024
47.9%

Most occurring characters

ValueCountFrequency (%)
24713
15.5%
a 12950
 
8.1%
n 11841
 
7.4%
s 10448
 
6.5%
t 10280
 
6.4%
e 9263
 
5.8%
o 7820
 
4.9%
i 7477
 
4.7%
r 7271
 
4.5%
u 5505
 
3.4%
Other values (61) 52262
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 97632
61.1%
Space Separator 24713
 
15.5%
Decimal Number 21368
 
13.4%
Uppercase Letter 10810
 
6.8%
Other Punctuation 4854
 
3.0%
Dash Punctuation 441
 
0.3%
Open Punctuation 6
 
< 0.1%
Close Punctuation 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12950
13.3%
n 11841
12.1%
s 10448
10.7%
t 10280
10.5%
e 9263
9.5%
o 7820
8.0%
i 7477
7.7%
r 7271
7.4%
u 5505
5.6%
f 5226
5.4%
Other values (16) 9551
9.8%
Uppercase Letter
ValueCountFrequency (%)
R 4399
40.7%
C 708
 
6.5%
S 640
 
5.9%
B 631
 
5.8%
M 564
 
5.2%
P 482
 
4.5%
L 477
 
4.4%
T 429
 
4.0%
A 353
 
3.3%
D 261
 
2.4%
Other values (16) 1866
17.3%
Decimal Number
ValueCountFrequency (%)
1 4359
20.4%
2 3017
14.1%
3 2476
11.6%
4 2030
9.5%
5 1840
8.6%
6 1799
8.4%
7 1579
 
7.4%
0 1481
 
6.9%
8 1453
 
6.8%
9 1334
 
6.2%
Other Punctuation
ValueCountFrequency (%)
# 4585
94.5%
& 191
 
3.9%
' 39
 
0.8%
\ 35
 
0.7%
. 4
 
0.1%
Space Separator
ValueCountFrequency (%)
24713
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 441
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 108442
67.8%
Common 51388
32.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12950
11.9%
n 11841
10.9%
s 10448
9.6%
t 10280
9.5%
e 9263
8.5%
o 7820
 
7.2%
i 7477
 
6.9%
r 7271
 
6.7%
u 5505
 
5.1%
f 5226
 
4.8%
Other values (42) 20361
18.8%
Common
ValueCountFrequency (%)
24713
48.1%
# 4585
 
8.9%
1 4359
 
8.5%
2 3017
 
5.9%
3 2476
 
4.8%
4 2030
 
4.0%
5 1840
 
3.6%
6 1799
 
3.5%
7 1579
 
3.1%
0 1481
 
2.9%
Other values (9) 3509
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24713
15.5%
a 12950
 
8.1%
n 11841
 
7.4%
s 10448
 
6.5%
t 10280
 
6.4%
e 9263
 
5.8%
o 7820
 
4.9%
i 7477
 
4.7%
r 7271
 
4.5%
u 5505
 
3.4%
Other values (61) 52262
32.7%

popularity_generic
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct4574
Distinct (%)100.0%
Missing426
Missing (%)8.5%
Memory size78.1 KiB
#810 of 2452 places to eat in Birmingham
 
1
#378 of 479 places to eat in Treviso
 
1
#23 of 1953 places to eat in Nice
 
1
#547 of 1117 places to eat in Cordoba
 
1
#1 of 2 places to eat in Constantine Bay
 
1
Other values (4569)
4569 

Length

Max length65
Median length55
Mean length37.119808
Min length28

Characters and Unicode

Total characters169786
Distinct characters69
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4574 ?
Unique (%)100.0%

Sample

1st row#810 of 2452 places to eat in Birmingham
2nd row#20 of 144 places to eat in El Albir
3rd row#2 of 3 places to eat in Kaloi Limenes
4th row#3 of 5 places to eat in Nea Madytos
5th row#9 of 10 places to eat in Valguarnera Caropepe

Common Values

ValueCountFrequency (%)
#810 of 2452 places to eat in Birmingham 1
 
< 0.1%
#378 of 479 places to eat in Treviso 1
 
< 0.1%
#23 of 1953 places to eat in Nice 1
 
< 0.1%
#547 of 1117 places to eat in Cordoba 1
 
< 0.1%
#1 of 2 places to eat in Constantine Bay 1
 
< 0.1%
#1454 of 1824 places to eat in Ibiza 1
 
< 0.1%
#5 of 7 places to eat in Marina Palmense 1
 
< 0.1%
#5 of 55 places to eat in Chelles 1
 
< 0.1%
#706 of 1531 places to eat in Las Palmas de Gran Canaria 1
 
< 0.1%
#10 of 22 places to eat in Somerton 1
 
< 0.1%
Other values (4564) 4564
91.3%
(Missing) 426
 
8.5%

Length

2023-01-27T10:27:50.903426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in 4584
 
12.1%
of 4576
 
12.1%
places 4574
 
12.1%
to 4574
 
12.1%
eat 4574
 
12.1%
1 349
 
0.9%
2 263
 
0.7%
3 247
 
0.7%
4 191
 
0.5%
6 161
 
0.4%
Other values (4695) 13705
36.3%

Most occurring characters

ValueCountFrequency (%)
33224
19.6%
a 13523
 
8.0%
e 13332
 
7.9%
o 12100
 
7.1%
t 10807
 
6.4%
n 7658
 
4.5%
i 7239
 
4.3%
l 6889
 
4.1%
s 6380
 
3.8%
c 5399
 
3.2%
Other values (59) 53235
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 102933
60.6%
Space Separator 33224
 
19.6%
Decimal Number 22774
 
13.4%
Uppercase Letter 5787
 
3.4%
Other Punctuation 4617
 
2.7%
Dash Punctuation 439
 
0.3%
Open Punctuation 6
 
< 0.1%
Close Punctuation 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13523
13.1%
e 13332
13.0%
o 12100
11.8%
t 10807
10.5%
n 7658
7.4%
i 7239
7.0%
l 6889
6.7%
s 6380
6.2%
c 5399
 
5.2%
p 4899
 
4.8%
Other values (16) 14707
14.3%
Uppercase Letter
ValueCountFrequency (%)
M 559
 
9.7%
S 543
 
9.4%
C 515
 
8.9%
B 491
 
8.5%
L 477
 
8.2%
P 477
 
8.2%
A 351
 
6.1%
G 244
 
4.2%
T 243
 
4.2%
R 235
 
4.1%
Other values (16) 1652
28.5%
Decimal Number
ValueCountFrequency (%)
1 4424
19.4%
2 3132
13.8%
3 2672
11.7%
4 2292
10.1%
5 2062
9.1%
6 1980
8.7%
8 1674
 
7.4%
7 1606
 
7.1%
0 1505
 
6.6%
9 1427
 
6.3%
Other Punctuation
ValueCountFrequency (%)
# 4574
99.1%
' 39
 
0.8%
. 4
 
0.1%
Space Separator
ValueCountFrequency (%)
33224
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 439
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 108720
64.0%
Common 61066
36.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13523
12.4%
e 13332
12.3%
o 12100
11.1%
t 10807
9.9%
n 7658
 
7.0%
i 7239
 
6.7%
l 6889
 
6.3%
s 6380
 
5.9%
c 5399
 
5.0%
p 4899
 
4.5%
Other values (42) 20494
18.9%
Common
ValueCountFrequency (%)
33224
54.4%
# 4574
 
7.5%
1 4424
 
7.2%
2 3132
 
5.1%
3 2672
 
4.4%
4 2292
 
3.8%
5 2062
 
3.4%
6 1980
 
3.2%
8 1674
 
2.7%
7 1606
 
2.6%
Other values (7) 3426
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 169786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33224
19.6%
a 13523
 
8.0%
e 13332
 
7.9%
o 12100
 
7.1%
t 10807
 
6.4%
n 7658
 
4.5%
i 7239
 
4.3%
l 6889
 
4.1%
s 6380
 
3.8%
c 5399
 
3.2%
Other values (59) 53235
31.4%

top_tags
Categorical

HIGH CARDINALITY  MISSING 

Distinct1657
Distinct (%)36.5%
Missing460
Missing (%)9.2%
Memory size78.1 KiB
Mid-range, French
 
112
Mid-range
 
98
Cheap Eats
 
89
Italian
 
72
Mid-range, Italian, Seafood, Mediterranean
 
71
Other values (1652)
4098 

Length

Max length67
Median length51
Mean length29.965419
Min length3

Characters and Unicode

Total characters136043
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1110 ?
Unique (%)24.4%

Sample

1st rowCheap Eats, Cafe
2nd rowMid-range, Mediterranean, European, Greek
3rd rowPizza, Seafood, Mediterranean, Greek
4th rowCheap Eats, Italian, Greek
5th rowCheap Eats, Cafe, Fast food

Common Values

ValueCountFrequency (%)
Mid-range, French 112
 
2.2%
Mid-range 98
 
2.0%
Cheap Eats 89
 
1.8%
Italian 72
 
1.4%
Mid-range, Italian, Seafood, Mediterranean 71
 
1.4%
Mid-range, Bar, British, Pub 67
 
1.3%
Mid-range, Italian 63
 
1.3%
Mid-range, Italian, Pizza, Seafood 60
 
1.2%
Cafe 54
 
1.1%
Mid-range, Italian, Pizza, Mediterranean 47
 
0.9%
Other values (1647) 3807
76.1%
(Missing) 460
 
9.2%

Length

2023-01-27T10:27:51.025904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mid-range 2557
 
15.5%
cheap 1107
 
6.7%
eats 1107
 
6.7%
italian 1068
 
6.5%
european 905
 
5.5%
mediterranean 719
 
4.3%
friendly 672
 
4.1%
vegetarian 672
 
4.1%
french 504
 
3.0%
pizza 497
 
3.0%
Other values (139) 6725
40.7%

Most occurring characters

ValueCountFrequency (%)
a 15551
 
11.4%
e 13487
 
9.9%
11993
 
8.8%
n 11194
 
8.2%
i 9804
 
7.2%
, 9296
 
6.8%
r 9177
 
6.7%
t 5631
 
4.1%
d 4823
 
3.5%
s 3792
 
2.8%
Other values (43) 41295
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 95859
70.5%
Uppercase Letter 16305
 
12.0%
Space Separator 11993
 
8.8%
Other Punctuation 9297
 
6.8%
Dash Punctuation 2589
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 15551
16.2%
e 13487
14.1%
n 11194
11.7%
i 9804
10.2%
r 9177
9.6%
t 5631
 
5.9%
d 4823
 
5.0%
s 3792
 
4.0%
g 3620
 
3.8%
h 3020
 
3.2%
Other values (16) 15760
16.4%
Uppercase Letter
ValueCountFrequency (%)
M 3380
20.7%
E 2074
12.7%
C 1945
11.9%
F 1651
10.1%
I 1385
8.5%
B 1195
 
7.3%
S 1020
 
6.3%
P 935
 
5.7%
V 774
 
4.7%
A 432
 
2.6%
Other values (13) 1514
9.3%
Other Punctuation
ValueCountFrequency (%)
, 9296
> 99.9%
& 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
11993
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2589
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 112164
82.4%
Common 23879
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 15551
13.9%
e 13487
12.0%
n 11194
 
10.0%
i 9804
 
8.7%
r 9177
 
8.2%
t 5631
 
5.0%
d 4823
 
4.3%
s 3792
 
3.4%
g 3620
 
3.2%
M 3380
 
3.0%
Other values (39) 31705
28.3%
Common
ValueCountFrequency (%)
11993
50.2%
, 9296
38.9%
- 2589
 
10.8%
& 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 15551
 
11.4%
e 13487
 
9.9%
11993
 
8.8%
n 11194
 
8.2%
i 9804
 
7.2%
, 9296
 
6.8%
r 9177
 
6.7%
t 5631
 
4.1%
d 4823
 
3.5%
s 3792
 
2.8%
Other values (43) 41295
30.4%

price_level
Categorical

Distinct3
Distinct (%)0.1%
Missing1211
Missing (%)24.2%
Memory size78.1 KiB
€€-€€€
2556 
1104 
€€€€
 
129

Length

Max length6
Median length6
Mean length4.4750594
Min length1

Characters and Unicode

Total characters16956
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row€€-€€€
3rd row
4th row
5th row€€-€€€

Common Values

ValueCountFrequency (%)
€€-€€€ 2556
51.1%
1104
22.1%
€€€€ 129
 
2.6%
(Missing) 1211
24.2%

Length

2023-01-27T10:27:51.127628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T10:27:51.211450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
€€-€€€ 2556
67.5%
1104
29.1%
€€€€ 129
 
3.4%

Most occurring characters

ValueCountFrequency (%)
14400
84.9%
- 2556
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Currency Symbol 14400
84.9%
Dash Punctuation 2556
 
15.1%

Most frequent character per category

Currency Symbol
ValueCountFrequency (%)
14400
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2556
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16956
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
14400
84.9%
- 2556
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
Currency Symbols 14400
84.9%
ASCII 2556
 
15.1%

Most frequent character per block

Currency Symbols
ValueCountFrequency (%)
14400
100.0%
ASCII
ValueCountFrequency (%)
- 2556
100.0%

price_range
Categorical

HIGH CARDINALITY  MISSING 

Distinct493
Distinct (%)35.0%
Missing3592
Missing (%)71.8%
Memory size78.1 KiB
€5-€15
 
29
€10-€30
 
29
€15-€30
 
26
€5-€10
 
22
€10-€20
 
19
Other values (488)
1283 

Length

Max length13
Median length6
Mean length6.4119318
Min length5

Characters and Unicode

Total characters9028
Distinct characters17
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique265 ?
Unique (%)18.8%

Sample

1st row€2-€11
2nd row€9-€20
3rd row€15-€45
4th row€3-€13
5th row€5-€10

Common Values

ValueCountFrequency (%)
€5-€15 29
 
0.6%
€10-€30 29
 
0.6%
€15-€30 26
 
0.5%
€5-€10 22
 
0.4%
€10-€20 19
 
0.4%
€5-€20 19
 
0.4%
€10-€25 18
 
0.4%
€3-€10 14
 
0.3%
€20-€40 14
 
0.3%
€6-€12 14
 
0.3%
Other values (483) 1204
 
24.1%
(Missing) 3592
71.8%

Length

2023-01-27T10:27:51.295970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
€5-€15 29
 
2.0%
€10-€30 29
 
2.0%
€15-€30 26
 
1.8%
€5-€10 22
 
1.5%
€10-€20 19
 
1.3%
€5-€20 19
 
1.3%
€10-€25 18
 
1.2%
chf 18
 
1.2%
€3-€10 14
 
1.0%
€20-€40 14
 
1.0%
Other values (492) 1236
85.6%

Most occurring characters

ValueCountFrequency (%)
2780
30.8%
- 1408
15.6%
1 960
 
10.6%
2 809
 
9.0%
0 682
 
7.6%
5 666
 
7.4%
3 478
 
5.3%
4 294
 
3.3%
6 217
 
2.4%
8 216
 
2.4%
Other values (7) 518
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4694
52.0%
Currency Symbol 2780
30.8%
Dash Punctuation 1408
 
15.6%
Uppercase Letter 108
 
1.2%
Space Separator 36
 
0.4%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 960
20.5%
2 809
17.2%
0 682
14.5%
5 666
14.2%
3 478
10.2%
4 294
 
6.3%
6 217
 
4.6%
8 216
 
4.6%
7 209
 
4.5%
9 163
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
C 36
33.3%
H 36
33.3%
F 36
33.3%
Currency Symbol
ValueCountFrequency (%)
2780
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1408
100.0%
Space Separator
ValueCountFrequency (%)
  36
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8920
98.8%
Latin 108
 
1.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2780
31.2%
- 1408
15.8%
1 960
 
10.8%
2 809
 
9.1%
0 682
 
7.6%
5 666
 
7.5%
3 478
 
5.4%
4 294
 
3.3%
6 217
 
2.4%
8 216
 
2.4%
Other values (4) 410
 
4.6%
Latin
ValueCountFrequency (%)
C 36
33.3%
H 36
33.3%
F 36
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6212
68.8%
Currency Symbols 2780
30.8%
None 36
 
0.4%

Most frequent character per block

Currency Symbols
ValueCountFrequency (%)
2780
100.0%
ASCII
ValueCountFrequency (%)
- 1408
22.7%
1 960
15.5%
2 809
13.0%
0 682
11.0%
5 666
10.7%
3 478
 
7.7%
4 294
 
4.7%
6 217
 
3.5%
8 216
 
3.5%
7 209
 
3.4%
Other values (5) 273
 
4.4%
None
ValueCountFrequency (%)
  36
100.0%

meals
Categorical

HIGH CARDINALITY  MISSING 

Distinct153
Distinct (%)5.1%
Missing1989
Missing (%)39.8%
Memory size78.1 KiB
Lunch, Dinner
960 
Dinner
335 
Breakfast, Lunch, Dinner
258 
Lunch, Dinner, After-hours
147 
Lunch
120 
Other values (148)
1191 

Length

Max length53
Median length42
Mean length17.729658
Min length5

Characters and Unicode

Total characters53384
Distinct characters20
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)2.1%

Sample

1st rowLunch, Dinner
2nd rowDrinks
3rd rowLunch, Breakfast
4th rowBreakfast, Lunch, Brunch
5th rowLunch, Dinner, After-hours, Drinks

Common Values

ValueCountFrequency (%)
Lunch, Dinner 960
19.2%
Dinner 335
 
6.7%
Breakfast, Lunch, Dinner 258
 
5.2%
Lunch, Dinner, After-hours 147
 
2.9%
Lunch 120
 
2.4%
Dinner, Lunch 119
 
2.4%
Lunch, Dinner, Drinks 81
 
1.6%
Breakfast, Lunch 80
 
1.6%
Breakfast 63
 
1.3%
Lunch, Dinner, After-hours, Drinks 52
 
1.0%
Other values (143) 796
15.9%
(Missing) 1989
39.8%

Length

2023-01-27T10:27:51.396690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dinner 2506
35.1%
lunch 2404
33.6%
breakfast 846
 
11.8%
drinks 502
 
7.0%
brunch 475
 
6.6%
after-hours 416
 
5.8%

Most occurring characters

ValueCountFrequency (%)
n 8393
15.7%
r 5161
9.7%
, 4138
 
7.8%
4138
 
7.8%
e 3768
 
7.1%
u 3295
 
6.2%
h 3295
 
6.2%
D 3008
 
5.6%
i 3008
 
5.6%
c 2879
 
5.4%
Other values (10) 12301
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37543
70.3%
Uppercase Letter 7149
 
13.4%
Other Punctuation 4138
 
7.8%
Space Separator 4138
 
7.8%
Dash Punctuation 416
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 8393
22.4%
r 5161
13.7%
e 3768
10.0%
u 3295
 
8.8%
h 3295
 
8.8%
i 3008
 
8.0%
c 2879
 
7.7%
s 1764
 
4.7%
a 1692
 
4.5%
k 1348
 
3.6%
Other values (3) 2940
 
7.8%
Uppercase Letter
ValueCountFrequency (%)
D 3008
42.1%
L 2404
33.6%
B 1321
18.5%
A 416
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 4138
100.0%
Space Separator
ValueCountFrequency (%)
4138
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 416
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44692
83.7%
Common 8692
 
16.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 8393
18.8%
r 5161
11.5%
e 3768
8.4%
u 3295
 
7.4%
h 3295
 
7.4%
D 3008
 
6.7%
i 3008
 
6.7%
c 2879
 
6.4%
L 2404
 
5.4%
s 1764
 
3.9%
Other values (7) 7717
17.3%
Common
ValueCountFrequency (%)
, 4138
47.6%
4138
47.6%
- 416
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 8393
15.7%
r 5161
9.7%
, 4138
 
7.8%
4138
 
7.8%
e 3768
 
7.1%
u 3295
 
6.2%
h 3295
 
6.2%
D 3008
 
5.6%
i 3008
 
5.6%
c 2879
 
5.4%
Other values (10) 12301
23.0%

cuisines
Categorical

HIGH CARDINALITY  MISSING 

Distinct1566
Distinct (%)36.9%
Missing761
Missing (%)15.2%
Memory size78.1 KiB
Italian
 
257
French
 
205
Cafe
 
167
Italian, Pizza
 
119
Spanish
 
107
Other values (1561)
3384 

Length

Max length119
Median length84
Mean length20.92687
Min length3

Characters and Unicode

Total characters88709
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1233 ?
Unique (%)29.1%

Sample

1st rowCafe
2nd rowMediterranean, European, Greek
3rd rowPizza, Seafood, Mediterranean, Greek, Russian
4th rowItalian, Greek
5th rowCafe, Fast food

Common Values

ValueCountFrequency (%)
Italian 257
 
5.1%
French 205
 
4.1%
Cafe 167
 
3.3%
Italian, Pizza 119
 
2.4%
Spanish 107
 
2.1%
Bar, British, Pub 72
 
1.4%
Fast food 68
 
1.4%
German 67
 
1.3%
French, European 66
 
1.3%
Mediterranean, Spanish 62
 
1.2%
Other values (1556) 3049
61.0%
(Missing) 761
 
15.2%

Length

2023-01-27T10:27:51.510628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
european 1080
 
9.8%
italian 1065
 
9.7%
mediterranean 807
 
7.4%
bar 512
 
4.7%
pizza 503
 
4.6%
french 502
 
4.6%
cafe 494
 
4.5%
spanish 444
 
4.0%
pub 429
 
3.9%
seafood 369
 
3.4%
Other values (122) 4764
43.4%

Most occurring characters

ValueCountFrequency (%)
a 10832
12.2%
e 8052
 
9.1%
n 7941
 
9.0%
6730
 
7.6%
, 6057
 
6.8%
r 6045
 
6.8%
i 5994
 
6.8%
t 4181
 
4.7%
o 3455
 
3.9%
s 2459
 
2.8%
Other values (40) 26963
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 64913
73.2%
Uppercase Letter 10839
 
12.2%
Space Separator 6730
 
7.6%
Other Punctuation 6058
 
6.8%
Dash Punctuation 169
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10832
16.7%
e 8052
12.4%
n 7941
12.2%
r 6045
9.3%
i 5994
9.2%
t 4181
 
6.4%
o 3455
 
5.3%
s 2459
 
3.8%
u 2458
 
3.8%
l 2251
 
3.5%
Other values (15) 11245
17.3%
Uppercase Letter
ValueCountFrequency (%)
I 1535
14.2%
S 1265
11.7%
E 1167
10.8%
P 1095
10.1%
B 1088
10.0%
C 1004
9.3%
M 928
8.6%
F 847
7.8%
A 509
 
4.7%
G 446
 
4.1%
Other values (11) 955
8.8%
Other Punctuation
ValueCountFrequency (%)
, 6057
> 99.9%
& 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
6730
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 75752
85.4%
Common 12957
 
14.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10832
14.3%
e 8052
 
10.6%
n 7941
 
10.5%
r 6045
 
8.0%
i 5994
 
7.9%
t 4181
 
5.5%
o 3455
 
4.6%
s 2459
 
3.2%
u 2458
 
3.2%
l 2251
 
3.0%
Other values (36) 22084
29.2%
Common
ValueCountFrequency (%)
6730
51.9%
, 6057
46.7%
- 169
 
1.3%
& 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10832
12.2%
e 8052
 
9.1%
n 7941
 
9.0%
6730
 
7.6%
, 6057
 
6.8%
r 6045
 
6.8%
i 5994
 
6.8%
t 4181
 
4.7%
o 3455
 
3.9%
s 2459
 
2.8%
Other values (40) 26963
30.4%

special_diets
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct27
Distinct (%)1.7%
Missing3367
Missing (%)67.3%
Memory size78.1 KiB
Vegetarian Friendly
757 
Vegetarian Friendly, Vegan Options, Gluten Free Options
326 
Vegetarian Friendly, Vegan Options
246 
Vegetarian Friendly, Gluten Free Options
167 
Gluten Free Options
 
50
Other values (22)
87 

Length

Max length62
Median length55
Mean length31.508267
Min length5

Characters and Unicode

Total characters51453
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.8%

Sample

1st rowVegetarian Friendly, Vegan Options
2nd rowVegetarian Friendly
3rd rowVegetarian Friendly
4th rowVegetarian Friendly
5th rowVegetarian Friendly, Vegan Options

Common Values

ValueCountFrequency (%)
Vegetarian Friendly 757
 
15.1%
Vegetarian Friendly, Vegan Options, Gluten Free Options 326
 
6.5%
Vegetarian Friendly, Vegan Options 246
 
4.9%
Vegetarian Friendly, Gluten Free Options 167
 
3.3%
Gluten Free Options 50
 
1.0%
Vegetarian Friendly, Gluten Free Options, Vegan Options 13
 
0.3%
Gluten Free Options, Vegetarian Friendly 11
 
0.2%
Vegan Options, Vegetarian Friendly 11
 
0.2%
Vegetarian Friendly, Halal 10
 
0.2%
Vegan Options 8
 
0.2%
Other values (17) 34
 
0.7%
(Missing) 3367
67.3%

Length

2023-01-27T10:27:51.610416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vegetarian 1560
25.3%
friendly 1560
25.3%
options 1214
19.7%
vegan 627
10.2%
gluten 587
 
9.5%
free 587
 
9.5%
halal 29
 
0.5%
kosher 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 7071
13.7%
n 5548
10.8%
4534
 
8.8%
i 4334
 
8.4%
a 3805
 
7.4%
r 3710
 
7.2%
t 3361
 
6.5%
l 2205
 
4.3%
V 2187
 
4.3%
g 2187
 
4.3%
Other values (13) 12511
24.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39579
76.9%
Uppercase Letter 6167
 
12.0%
Space Separator 4534
 
8.8%
Other Punctuation 1173
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7071
17.9%
n 5548
14.0%
i 4334
11.0%
a 3805
9.6%
r 3710
9.4%
t 3361
8.5%
l 2205
 
5.6%
g 2187
 
5.5%
d 1560
 
3.9%
y 1560
 
3.9%
Other values (5) 4238
10.7%
Uppercase Letter
ValueCountFrequency (%)
V 2187
35.5%
F 2147
34.8%
O 1214
19.7%
G 587
 
9.5%
H 29
 
0.5%
K 3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
4534
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1173
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45746
88.9%
Common 5707
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7071
15.5%
n 5548
12.1%
i 4334
9.5%
a 3805
8.3%
r 3710
8.1%
t 3361
 
7.3%
l 2205
 
4.8%
V 2187
 
4.8%
g 2187
 
4.8%
F 2147
 
4.7%
Other values (11) 9191
20.1%
Common
ValueCountFrequency (%)
4534
79.4%
, 1173
 
20.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51453
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7071
13.7%
n 5548
10.8%
4534
 
8.8%
i 4334
 
8.4%
a 3805
 
7.4%
r 3710
 
7.2%
t 3361
 
6.5%
l 2205
 
4.3%
V 2187
 
4.3%
g 2187
 
4.3%
Other values (13) 12511
24.3%

features
Categorical

HIGH CARDINALITY  MISSING 

Distinct637
Distinct (%)43.2%
Missing3527
Missing (%)70.5%
Memory size78.1 KiB
Reservations
191 
Reservations, Seating, Table Service
 
76
Reservations, Seating, Serves Alcohol, Table Service
 
40
Reservations, Seating, Wheelchair Accessible, Table Service
 
32
Reservations, Seating, Wheelchair Accessible, Serves Alcohol, Table Service
 
30
Other values (632)
1104 

Length

Max length344
Median length268
Mean length67.452138
Min length6

Characters and Unicode

Total characters99357
Distinct characters44
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique505 ?
Unique (%)34.3%

Sample

1st rowReservations, Outdoor Seating, Seating, Wheelchair Accessible, Serves Alcohol, Full Bar, Accepts Credit Cards, Table Service
2nd rowReservations, Outdoor Seating, Buffet, Parking Available, Free off-street parking, Television, Highchairs Available, Wheelchair Accessible, Serves Alcohol, Full Bar, Wine and Beer, Free Wifi, Accepts Credit Cards
3rd rowReservations, Seating, Table Service
4th rowWheelchair Accessible
5th rowReservations

Common Values

ValueCountFrequency (%)
Reservations 191
 
3.8%
Reservations, Seating, Table Service 76
 
1.5%
Reservations, Seating, Serves Alcohol, Table Service 40
 
0.8%
Reservations, Seating, Wheelchair Accessible, Table Service 32
 
0.6%
Reservations, Seating, Wheelchair Accessible, Serves Alcohol, Table Service 30
 
0.6%
Takeout 28
 
0.6%
Wheelchair Accessible 26
 
0.5%
Seating 22
 
0.4%
Seating, Table Service 21
 
0.4%
Takeout, Wheelchair Accessible 21
 
0.4%
Other values (627) 986
 
19.7%
(Missing) 3527
70.5%

Length

2023-01-27T10:27:51.717171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
seating 1391
 
12.1%
reservations 1024
 
8.9%
table 892
 
7.8%
service 892
 
7.8%
wheelchair 666
 
5.8%
accessible 666
 
5.8%
serves 612
 
5.3%
alcohol 612
 
5.3%
accepts 469
 
4.1%
takeout 434
 
3.8%
Other values (46) 3797
33.1%

Most occurring characters

ValueCountFrequency (%)
e 13191
13.3%
9982
 
10.0%
i 7076
 
7.1%
a 6804
 
6.8%
r 5628
 
5.7%
s 5472
 
5.5%
, 5385
 
5.4%
l 4981
 
5.0%
c 4805
 
4.8%
t 4449
 
4.5%
Other values (34) 31584
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72698
73.2%
Uppercase Letter 11238
 
11.3%
Space Separator 9982
 
10.0%
Other Punctuation 5385
 
5.4%
Dash Punctuation 54
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13191
18.1%
i 7076
9.7%
a 6804
9.4%
r 5628
7.7%
s 5472
7.5%
l 4981
 
6.9%
c 4805
 
6.6%
t 4449
 
6.1%
o 3522
 
4.8%
n 3161
 
4.3%
Other values (12) 13609
18.7%
Uppercase Letter
ValueCountFrequency (%)
S 2962
26.4%
A 2158
19.2%
T 1413
12.6%
R 1024
 
9.1%
W 936
 
8.3%
C 573
 
5.1%
F 464
 
4.1%
B 355
 
3.2%
O 354
 
3.2%
P 286
 
2.5%
Other values (9) 713
 
6.3%
Space Separator
ValueCountFrequency (%)
9982
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5385
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83936
84.5%
Common 15421
 
15.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13191
15.7%
i 7076
 
8.4%
a 6804
 
8.1%
r 5628
 
6.7%
s 5472
 
6.5%
l 4981
 
5.9%
c 4805
 
5.7%
t 4449
 
5.3%
o 3522
 
4.2%
n 3161
 
3.8%
Other values (31) 24847
29.6%
Common
ValueCountFrequency (%)
9982
64.7%
, 5385
34.9%
- 54
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13191
13.3%
9982
 
10.0%
i 7076
 
7.1%
a 6804
 
6.8%
r 5628
 
5.7%
s 5472
 
5.5%
, 5385
 
5.4%
l 4981
 
5.0%
c 4805
 
4.8%
t 4449
 
4.5%
Other values (34) 31584
31.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
False
3440 
True
1560 
ValueCountFrequency (%)
False 3440
68.8%
True 1560
31.2%
2023-01-27T10:27:51.815208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
False
4373 
True
627 
ValueCountFrequency (%)
False 4373
87.5%
True 627
 
12.5%
2023-01-27T10:27:51.887922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
False
4413 
True
587 
ValueCountFrequency (%)
False 4413
88.3%
True 587
 
11.7%
2023-01-27T10:27:51.956073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

original_open_hours
Categorical

HIGH CARDINALITY  MISSING 

Distinct2264
Distinct (%)81.5%
Missing2222
Missing (%)44.4%
Memory size78.1 KiB
{"Mon": ["00:00-23:59"], "Tue": ["00:00-23:59"], "Wed": ["00:00-23:59"], "Thu": ["00:00-23:59"], "Fri": ["00:00-23:59"], "Sat": ["00:00-23:59"], "Sun": ["00:00-23:59"]}
 
31
{"Mon": ["11:00-23:00"], "Tue": ["11:00-23:00"], "Wed": ["11:00-23:00"], "Thu": ["11:00-23:00"], "Fri": ["11:00-23:00"], "Sat": ["11:00-23:00"], "Sun": ["11:00-23:00"]}
 
30
{"Mon": ["12:00-23:00"], "Tue": ["12:00-23:00"], "Wed": ["12:00-23:00"], "Thu": ["12:00-23:00"], "Fri": ["12:00-23:00"], "Sat": ["12:00-23:00"], "Sun": ["12:00-23:00"]}
 
21
{"Mon": ["12:00-22:00"], "Tue": ["12:00-22:00"], "Wed": ["12:00-22:00"], "Thu": ["12:00-22:00"], "Fri": ["12:00-22:00"], "Sat": ["12:00-22:00"], "Sun": ["12:00-22:00"]}
 
17
{"Mon": ["10:00-22:00"], "Tue": ["10:00-22:00"], "Wed": ["10:00-22:00"], "Thu": ["10:00-22:00"], "Fri": ["10:00-22:00"], "Sat": ["10:00-22:00"], "Sun": ["10:00-22:00"]}
 
14
Other values (2259)
2665 

Length

Max length273
Median length260
Mean length179.72642
Min length90

Characters and Unicode

Total characters499280
Distinct characters34
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2064 ?
Unique (%)74.3%

Sample

1st row{"Mon": ["13:30-23:00"], "Tue": ["13:30-23:00"], "Wed": [], "Thu": ["13:30-23:00"], "Fri": ["13:30-23:00"], "Sat": ["13:30-23:00"], "Sun": ["13:30-23:00"]}
2nd row{"Mon": ["11:00-22:00"], "Tue": ["11:00-22:00"], "Wed": ["11:00-22:00"], "Thu": ["11:00-22:00"], "Fri": ["11:00-22:00"], "Sat": ["11:00-22:00"], "Sun": ["12:00-22:00"]}
3rd row{"Mon": ["07:00-21:00"], "Tue": ["07:00-21:00"], "Wed": ["07:00-21:00"], "Thu": ["07:00-21:00"], "Fri": ["07:00-22:00"], "Sat": ["07:00-22:00"], "Sun": ["07:00-21:00"]}
4th row{"Mon": [], "Tue": ["08:30-16:30"], "Wed": ["08:30-16:30"], "Thu": ["08:30-16:30"], "Fri": ["08:30-16:30"], "Sat": ["09:00-15:00"], "Sun": []}
5th row{"Mon": [], "Tue": [], "Wed": [], "Thu": [], "Fri": ["11:00-13:00", "17:00-20:00"], "Sat": ["11:00-13:00", "17:00-20:00"], "Sun": ["11:00-13:00"]}

Common Values

ValueCountFrequency (%)
{"Mon": ["00:00-23:59"], "Tue": ["00:00-23:59"], "Wed": ["00:00-23:59"], "Thu": ["00:00-23:59"], "Fri": ["00:00-23:59"], "Sat": ["00:00-23:59"], "Sun": ["00:00-23:59"]} 31
 
0.6%
{"Mon": ["11:00-23:00"], "Tue": ["11:00-23:00"], "Wed": ["11:00-23:00"], "Thu": ["11:00-23:00"], "Fri": ["11:00-23:00"], "Sat": ["11:00-23:00"], "Sun": ["11:00-23:00"]} 30
 
0.6%
{"Mon": ["12:00-23:00"], "Tue": ["12:00-23:00"], "Wed": ["12:00-23:00"], "Thu": ["12:00-23:00"], "Fri": ["12:00-23:00"], "Sat": ["12:00-23:00"], "Sun": ["12:00-23:00"]} 21
 
0.4%
{"Mon": ["12:00-22:00"], "Tue": ["12:00-22:00"], "Wed": ["12:00-22:00"], "Thu": ["12:00-22:00"], "Fri": ["12:00-22:00"], "Sat": ["12:00-22:00"], "Sun": ["12:00-22:00"]} 17
 
0.3%
{"Mon": ["10:00-22:00"], "Tue": ["10:00-22:00"], "Wed": ["10:00-22:00"], "Thu": ["10:00-22:00"], "Fri": ["10:00-22:00"], "Sat": ["10:00-22:00"], "Sun": ["10:00-22:00"]} 14
 
0.3%
{"Mon": ["10:00-23:00"], "Tue": ["10:00-23:00"], "Wed": ["10:00-23:00"], "Thu": ["10:00-23:00"], "Fri": ["10:00-23:00"], "Sat": ["10:00-23:00"], "Sun": ["10:00-23:00"]} 12
 
0.2%
{"Mon": ["11:00-00:00"], "Tue": ["11:00-00:00"], "Wed": ["11:00-00:00"], "Thu": ["11:00-00:00"], "Fri": ["11:00-00:00"], "Sat": ["11:00-00:00"], "Sun": ["11:00-00:00"]} 12
 
0.2%
{"Mon": ["12:00-00:00"], "Tue": ["12:00-00:00"], "Wed": ["12:00-00:00"], "Thu": ["12:00-00:00"], "Fri": ["12:00-00:00"], "Sat": ["12:00-00:00"], "Sun": ["12:00-00:00"]} 11
 
0.2%
{"Mon": ["17:00-23:00"], "Tue": ["17:00-23:00"], "Wed": ["17:00-23:00"], "Thu": ["17:00-23:00"], "Fri": ["17:00-23:00"], "Sat": ["17:00-23:00"], "Sun": ["17:00-23:00"]} 10
 
0.2%
{"Mon": ["11:00-22:00"], "Tue": ["11:00-22:00"], "Wed": ["11:00-22:00"], "Thu": ["11:00-22:00"], "Fri": ["11:00-22:00"], "Sat": ["11:00-22:00"], "Sun": ["11:00-22:00"]} 10
 
0.2%
Other values (2254) 2610
52.2%
(Missing) 2222
44.4%

Length

2023-01-27T10:27:52.046131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mon 2778
 
6.5%
tue 2778
 
6.5%
wed 2778
 
6.5%
thu 2778
 
6.5%
fri 2778
 
6.5%
sat 2778
 
6.5%
sun 2778
 
6.5%
1888
 
4.4%
12:00-14:30 565
 
1.3%
12:00-14:00 555
 
1.3%
Other values (812) 20246
47.4%

Most occurring characters

ValueCountFrequency (%)
0 87743
17.6%
" 81624
16.3%
: 62178
12.5%
39922
8.0%
1 28333
 
5.7%
- 21366
 
4.3%
, 20476
 
4.1%
2 19638
 
3.9%
[ 19446
 
3.9%
] 19446
 
3.9%
Other values (24) 99108
19.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 170928
34.2%
Other Punctuation 164278
32.9%
Space Separator 39922
 
8.0%
Lowercase Letter 38892
 
7.8%
Open Punctuation 22224
 
4.5%
Close Punctuation 22224
 
4.5%
Dash Punctuation 21366
 
4.3%
Uppercase Letter 19446
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 87743
51.3%
1 28333
 
16.6%
2 19638
 
11.5%
3 15130
 
8.9%
9 4936
 
2.9%
8 3580
 
2.1%
5 3346
 
2.0%
7 3162
 
1.8%
4 3140
 
1.8%
6 1920
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
u 8334
21.4%
n 5556
14.3%
e 5556
14.3%
h 2778
 
7.1%
r 2778
 
7.1%
i 2778
 
7.1%
d 2778
 
7.1%
a 2778
 
7.1%
t 2778
 
7.1%
o 2778
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
S 5556
28.6%
T 5556
28.6%
F 2778
14.3%
W 2778
14.3%
M 2778
14.3%
Other Punctuation
ValueCountFrequency (%)
" 81624
49.7%
: 62178
37.8%
, 20476
 
12.5%
Open Punctuation
ValueCountFrequency (%)
[ 19446
87.5%
{ 2778
 
12.5%
Close Punctuation
ValueCountFrequency (%)
] 19446
87.5%
} 2778
 
12.5%
Space Separator
ValueCountFrequency (%)
39922
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21366
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 440942
88.3%
Latin 58338
 
11.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 87743
19.9%
" 81624
18.5%
: 62178
14.1%
39922
9.1%
1 28333
 
6.4%
- 21366
 
4.8%
, 20476
 
4.6%
2 19638
 
4.5%
[ 19446
 
4.4%
] 19446
 
4.4%
Other values (9) 40770
9.2%
Latin
ValueCountFrequency (%)
u 8334
14.3%
n 5556
 
9.5%
S 5556
 
9.5%
T 5556
 
9.5%
e 5556
 
9.5%
h 2778
 
4.8%
F 2778
 
4.8%
r 2778
 
4.8%
i 2778
 
4.8%
d 2778
 
4.8%
Other values (5) 13890
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 499280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 87743
17.6%
" 81624
16.3%
: 62178
12.5%
39922
8.0%
1 28333
 
5.7%
- 21366
 
4.3%
, 20476
 
4.1%
2 19638
 
3.9%
[ 19446
 
3.9%
] 19446
 
3.9%
Other values (24) 99108
19.9%

open_days_per_week
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.3%
Missing2222
Missing (%)44.4%
Infinite0
Infinite (%)0.0%
Mean6.3203744
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:52.662726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q16
median7
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.97518118
Coefficient of variation (CV)0.15429168
Kurtosis6.1820247
Mean6.3203744
Median Absolute Deviation (MAD)0
Skewness-2.1261365
Sum17558
Variance0.95097833
MonotonicityNot monotonic
2023-01-27T10:27:52.731399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 1513
30.3%
6 875
 
17.5%
5 265
 
5.3%
4 60
 
1.2%
3 33
 
0.7%
2 21
 
0.4%
1 11
 
0.2%
(Missing) 2222
44.4%
ValueCountFrequency (%)
1 11
 
0.2%
2 21
 
0.4%
3 33
 
0.7%
4 60
 
1.2%
5 265
 
5.3%
6 875
17.5%
7 1513
30.3%
ValueCountFrequency (%)
7 1513
30.3%
6 875
17.5%
5 265
 
5.3%
4 60
 
1.2%
3 33
 
0.7%
2 21
 
0.4%
1 11
 
0.2%

open_hours_per_week
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct366
Distinct (%)13.2%
Missing2222
Missing (%)44.4%
Infinite0
Infinite (%)0.0%
Mean61.676308
Minimum0
Maximum167.88333
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:52.825469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q139
median58
Q381
95-th percentile112
Maximum167.88333
Range167.88333
Interquartile range (IQR)42

Descriptive statistics

Standard deviation30.357966
Coefficient of variation (CV)0.49221438
Kurtosis0.68118495
Mean61.676308
Median Absolute Deviation (MAD)20.625
Skewness0.68444766
Sum171336.78
Variance921.60609
MonotonicityNot monotonic
2023-01-27T10:27:52.929161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 90
 
1.8%
42 78
 
1.6%
77 58
 
1.2%
49 56
 
1.1%
70 52
 
1.0%
63 51
 
1.0%
30 50
 
1.0%
48 46
 
0.9%
91 44
 
0.9%
56 43
 
0.9%
Other values (356) 2210
44.2%
(Missing) 2222
44.4%
ValueCountFrequency (%)
0 7
0.1%
0.25 3
0.1%
1 1
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
5.25 1
 
< 0.1%
6 3
0.1%
6.75 1
 
< 0.1%
7 3
0.1%
ValueCountFrequency (%)
167.8833333 31
0.6%
161 1
 
< 0.1%
154 1
 
< 0.1%
146 1
 
< 0.1%
145 1
 
< 0.1%
144 1
 
< 0.1%
143.9 8
 
0.2%
141 1
 
< 0.1%
140.4833333 1
 
< 0.1%
140 5
 
0.1%

working_shifts_per_week
Real number (ℝ)

Distinct14
Distinct (%)0.5%
Missing2222
Missing (%)44.4%
Infinite0
Infinite (%)0.0%
Mean7.6911447
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:53.017131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q16
median7
Q38
95-th percentile14
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.6057056
Coefficient of variation (CV)0.33879295
Kurtosis0.63142103
Mean7.6911447
Median Absolute Deviation (MAD)1
Skewness1.0428765
Sum21366
Variance6.7897019
MonotonicityNot monotonic
2023-01-27T10:27:53.095167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
7 1254
25.1%
6 565
 
11.3%
14 173
 
3.5%
12 164
 
3.3%
5 155
 
3.1%
11 113
 
2.3%
10 80
 
1.6%
9 65
 
1.3%
8 52
 
1.0%
13 51
 
1.0%
Other values (4) 106
 
2.1%
(Missing) 2222
44.4%
ValueCountFrequency (%)
1 11
 
0.2%
2 19
 
0.4%
3 27
 
0.5%
4 49
 
1.0%
5 155
 
3.1%
6 565
11.3%
7 1254
25.1%
8 52
 
1.0%
9 65
 
1.3%
10 80
 
1.6%
ValueCountFrequency (%)
14 173
 
3.5%
13 51
 
1.0%
12 164
 
3.3%
11 113
 
2.3%
10 80
 
1.6%
9 65
 
1.3%
8 52
 
1.0%
7 1254
25.1%
6 565
11.3%
5 155
 
3.1%

avg_rating
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)0.2%
Missing419
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean4.0323074
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:53.172045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q13.5
median4
Q34.5
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72074963
Coefficient of variation (CV)0.17874372
Kurtosis2.3800204
Mean4.0323074
Median Absolute Deviation (MAD)0.5
Skewness-1.2419274
Sum18472
Variance0.51948002
MonotonicityNot monotonic
2023-01-27T10:27:53.242815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4.5 1410
28.2%
4 1391
27.8%
3.5 675
13.5%
5 560
 
11.2%
3 293
 
5.9%
2.5 128
 
2.6%
2 64
 
1.3%
1 33
 
0.7%
1.5 27
 
0.5%
(Missing) 419
 
8.4%
ValueCountFrequency (%)
1 33
 
0.7%
1.5 27
 
0.5%
2 64
 
1.3%
2.5 128
 
2.6%
3 293
 
5.9%
3.5 675
13.5%
4 1391
27.8%
4.5 1410
28.2%
5 560
 
11.2%
ValueCountFrequency (%)
5 560
 
11.2%
4.5 1410
28.2%
4 1391
27.8%
3.5 675
13.5%
3 293
 
5.9%
2.5 128
 
2.6%
2 64
 
1.3%
1.5 27
 
0.5%
1 33
 
0.7%

total_reviews_count
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct609
Distinct (%)12.8%
Missing234
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean102.93642
Minimum0
Maximum4192
Zeros185
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:53.343893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median26
Q393
95-th percentile450
Maximum4192
Range4192
Interquartile range (IQR)87

Descriptive statistics

Standard deviation235.67232
Coefficient of variation (CV)2.289494
Kurtosis68.751866
Mean102.93642
Median Absolute Deviation (MAD)24
Skewness6.5585197
Sum490595
Variance55541.444
MonotonicityNot monotonic
2023-01-27T10:27:53.447940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 276
 
5.5%
2 203
 
4.1%
0 185
 
3.7%
3 157
 
3.1%
4 136
 
2.7%
5 128
 
2.6%
6 112
 
2.2%
8 108
 
2.2%
7 105
 
2.1%
9 86
 
1.7%
Other values (599) 3270
65.4%
(Missing) 234
 
4.7%
ValueCountFrequency (%)
0 185
3.7%
1 276
5.5%
2 203
4.1%
3 157
3.1%
4 136
2.7%
5 128
2.6%
6 112
2.2%
7 105
 
2.1%
8 108
 
2.2%
9 86
 
1.7%
ValueCountFrequency (%)
4192 1
< 0.1%
3668 1
< 0.1%
3576 1
< 0.1%
3536 1
< 0.1%
2410 1
< 0.1%
2374 1
< 0.1%
2354 1
< 0.1%
2344 1
< 0.1%
2186 1
< 0.1%
2166 1
< 0.1%

default_language
Categorical

Distinct2
Distinct (%)< 0.1%
Missing416
Missing (%)8.3%
Memory size78.1 KiB
English
3215 
All languages
1369 

Length

Max length13
Median length7
Mean length8.7918848
Min length7

Characters and Unicode

Total characters40302
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowAll languages
4th rowAll languages
5th rowAll languages

Common Values

ValueCountFrequency (%)
English 3215
64.3%
All languages 1369
27.4%
(Missing) 416
 
8.3%

Length

2023-01-27T10:27:53.546801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T10:27:53.636685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
english 3215
54.0%
all 1369
23.0%
languages 1369
23.0%

Most occurring characters

ValueCountFrequency (%)
l 7322
18.2%
g 5953
14.8%
n 4584
11.4%
s 4584
11.4%
E 3215
8.0%
i 3215
8.0%
h 3215
8.0%
a 2738
 
6.8%
A 1369
 
3.4%
1369
 
3.4%
Other values (2) 2738
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34349
85.2%
Uppercase Letter 4584
 
11.4%
Space Separator 1369
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 7322
21.3%
g 5953
17.3%
n 4584
13.3%
s 4584
13.3%
i 3215
9.4%
h 3215
9.4%
a 2738
 
8.0%
u 1369
 
4.0%
e 1369
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
E 3215
70.1%
A 1369
29.9%
Space Separator
ValueCountFrequency (%)
1369
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38933
96.6%
Common 1369
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 7322
18.8%
g 5953
15.3%
n 4584
11.8%
s 4584
11.8%
E 3215
8.3%
i 3215
8.3%
h 3215
8.3%
a 2738
 
7.0%
A 1369
 
3.5%
u 1369
 
3.5%
Common
ValueCountFrequency (%)
1369
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 7322
18.2%
g 5953
14.8%
n 4584
11.4%
s 4584
11.4%
E 3215
8.0%
i 3215
8.0%
h 3215
8.0%
a 2738
 
6.8%
A 1369
 
3.4%
1369
 
3.4%
Other values (2) 2738
 
6.8%

reviews_count_in_default_language
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct368
Distinct (%)8.0%
Missing416
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean44.273997
Minimum1
Maximum3031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:53.724697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q326
95-th percentile197.85
Maximum3031
Range3030
Interquartile range (IQR)24

Descriptive statistics

Standard deviation142.52562
Coefficient of variation (CV)3.2191723
Kurtosis118.34432
Mean44.273997
Median Absolute Deviation (MAD)6
Skewness8.9586004
Sum202952
Variance20313.553
MonotonicityNot monotonic
2023-01-27T10:27:53.833365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 768
15.4%
2 465
 
9.3%
3 310
 
6.2%
4 259
 
5.2%
5 205
 
4.1%
6 175
 
3.5%
7 155
 
3.1%
9 123
 
2.5%
8 121
 
2.4%
10 94
 
1.9%
Other values (358) 1909
38.2%
(Missing) 416
 
8.3%
ValueCountFrequency (%)
1 768
15.4%
2 465
9.3%
3 310
6.2%
4 259
 
5.2%
5 205
 
4.1%
6 175
 
3.5%
7 155
 
3.1%
8 121
 
2.4%
9 123
 
2.5%
10 94
 
1.9%
ValueCountFrequency (%)
3031 1
< 0.1%
2339 1
< 0.1%
2321 1
< 0.1%
2314 1
< 0.1%
1907 1
< 0.1%
1861 1
< 0.1%
1828 1
< 0.1%
1633 1
< 0.1%
1363 1
< 0.1%
1324 1
< 0.1%

excellent
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct271
Distinct (%)5.9%
Missing416
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean24.195899
Minimum0
Maximum1776
Zeros682
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:53.946174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q313
95-th percentile105
Maximum1776
Range1776
Interquartile range (IQR)12

Descriptive statistics

Standard deviation84.90881
Coefficient of variation (CV)3.5092232
Kurtosis127.45682
Mean24.195899
Median Absolute Deviation (MAD)3
Skewness9.4844159
Sum110914
Variance7209.506
MonotonicityNot monotonic
2023-01-27T10:27:54.052094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 823
16.5%
0 682
13.6%
2 479
 
9.6%
3 334
 
6.7%
4 225
 
4.5%
5 188
 
3.8%
6 156
 
3.1%
7 133
 
2.7%
8 107
 
2.1%
9 91
 
1.8%
Other values (261) 1366
27.3%
(Missing) 416
 
8.3%
ValueCountFrequency (%)
0 682
13.6%
1 823
16.5%
2 479
9.6%
3 334
6.7%
4 225
 
4.5%
5 188
 
3.8%
6 156
 
3.1%
7 133
 
2.7%
8 107
 
2.1%
9 91
 
1.8%
ValueCountFrequency (%)
1776 1
< 0.1%
1538 1
< 0.1%
1429 1
< 0.1%
1334 1
< 0.1%
1068 1
< 0.1%
1055 1
< 0.1%
1053 1
< 0.1%
1047 1
< 0.1%
1040 1
< 0.1%
883 1
< 0.1%

very_good
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct162
Distinct (%)3.5%
Missing416
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean10.681719
Minimum0
Maximum880
Zeros1231
Zeros (%)24.6%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:54.168279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile47
Maximum880
Range880
Interquartile range (IQR)6

Descriptive statistics

Standard deviation35.596732
Coefficient of variation (CV)3.332491
Kurtosis160.65248
Mean10.681719
Median Absolute Deviation (MAD)2
Skewness10.18218
Sum48965
Variance1267.1273
MonotonicityNot monotonic
2023-01-27T10:27:54.270220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1231
24.6%
1 928
18.6%
2 498
10.0%
3 299
 
6.0%
4 228
 
4.6%
5 171
 
3.4%
6 123
 
2.5%
7 99
 
2.0%
8 72
 
1.4%
10 62
 
1.2%
Other values (152) 873
17.5%
(Missing) 416
 
8.3%
ValueCountFrequency (%)
0 1231
24.6%
1 928
18.6%
2 498
10.0%
3 299
 
6.0%
4 228
 
4.6%
5 171
 
3.4%
6 123
 
2.5%
7 99
 
2.0%
8 72
 
1.4%
9 62
 
1.2%
ValueCountFrequency (%)
880 1
< 0.1%
694 1
< 0.1%
538 1
< 0.1%
533 1
< 0.1%
498 1
< 0.1%
488 1
< 0.1%
464 1
< 0.1%
328 1
< 0.1%
322 1
< 0.1%
304 1
< 0.1%

average
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct96
Distinct (%)2.1%
Missing416
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean4.0811518
Minimum0
Maximum321
Zeros2249
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:54.373049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile18
Maximum321
Range321
Interquartile range (IQR)2

Descriptive statistics

Standard deviation14.266347
Coefficient of variation (CV)3.4956668
Kurtosis127.22659
Mean4.0811518
Median Absolute Deviation (MAD)1
Skewness9.3358744
Sum18708
Variance203.52865
MonotonicityNot monotonic
2023-01-27T10:27:54.478075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2249
45.0%
1 871
 
17.4%
2 379
 
7.6%
3 207
 
4.1%
4 133
 
2.7%
5 105
 
2.1%
7 60
 
1.2%
6 60
 
1.2%
8 49
 
1.0%
9 48
 
1.0%
Other values (86) 423
 
8.5%
(Missing) 416
 
8.3%
ValueCountFrequency (%)
0 2249
45.0%
1 871
 
17.4%
2 379
 
7.6%
3 207
 
4.1%
4 133
 
2.7%
5 105
 
2.1%
6 60
 
1.2%
7 60
 
1.2%
8 49
 
1.0%
9 48
 
1.0%
ValueCountFrequency (%)
321 1
< 0.1%
246 1
< 0.1%
212 1
< 0.1%
206 1
< 0.1%
193 1
< 0.1%
171 1
< 0.1%
170 1
< 0.1%
169 1
< 0.1%
160 1
< 0.1%
159 1
< 0.1%

poor
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct73
Distinct (%)1.6%
Missing416
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean2.3115183
Minimum0
Maximum164
Zeros2873
Zeros (%)57.5%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:54.589446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10
Maximum164
Range164
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.5215537
Coefficient of variation (CV)3.6865612
Kurtosis108.62804
Mean2.3115183
Median Absolute Deviation (MAD)0
Skewness8.8048151
Sum10596
Variance72.616878
MonotonicityNot monotonic
2023-01-27T10:27:54.695126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2873
57.5%
1 706
 
14.1%
2 273
 
5.5%
3 150
 
3.0%
4 107
 
2.1%
5 70
 
1.4%
7 50
 
1.0%
6 41
 
0.8%
9 40
 
0.8%
8 29
 
0.6%
Other values (63) 245
 
4.9%
(Missing) 416
 
8.3%
ValueCountFrequency (%)
0 2873
57.5%
1 706
 
14.1%
2 273
 
5.5%
3 150
 
3.0%
4 107
 
2.1%
5 70
 
1.4%
6 41
 
0.8%
7 50
 
1.0%
8 29
 
0.6%
9 40
 
0.8%
ValueCountFrequency (%)
164 1
< 0.1%
160 1
< 0.1%
135 1
< 0.1%
122 1
< 0.1%
105 1
< 0.1%
100 2
< 0.1%
92 1
< 0.1%
89 1
< 0.1%
84 1
< 0.1%
82 1
< 0.1%

terrible
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct85
Distinct (%)1.9%
Missing416
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean3.0037086
Minimum0
Maximum375
Zeros2632
Zeros (%)52.6%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:54.805087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile13
Maximum375
Range375
Interquartile range (IQR)2

Descriptive statistics

Standard deviation11.319618
Coefficient of variation (CV)3.7685472
Kurtosis305.64737
Mean3.0037086
Median Absolute Deviation (MAD)0
Skewness13.046438
Sum13769
Variance128.13374
MonotonicityNot monotonic
2023-01-27T10:27:54.908557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2632
52.6%
1 749
 
15.0%
2 319
 
6.4%
3 162
 
3.2%
4 114
 
2.3%
5 80
 
1.6%
8 58
 
1.2%
6 53
 
1.1%
7 53
 
1.1%
9 42
 
0.8%
Other values (75) 322
 
6.4%
(Missing) 416
 
8.3%
ValueCountFrequency (%)
0 2632
52.6%
1 749
 
15.0%
2 319
 
6.4%
3 162
 
3.2%
4 114
 
2.3%
5 80
 
1.6%
6 53
 
1.1%
7 53
 
1.1%
8 58
 
1.2%
9 42
 
0.8%
ValueCountFrequency (%)
375 1
< 0.1%
195 1
< 0.1%
165 1
< 0.1%
135 1
< 0.1%
121 1
< 0.1%
118 1
< 0.1%
109 1
< 0.1%
106 1
< 0.1%
105 1
< 0.1%
102 1
< 0.1%

food
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.3%
Missing2130
Missing (%)42.6%
Infinite0
Infinite (%)0.0%
Mean4.1027875
Minimum1.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:54.999346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile3
Q14
median4
Q34.5
95-th percentile5
Maximum5
Range3.5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.56355495
Coefficient of variation (CV)0.13735904
Kurtosis1.4457536
Mean4.1027875
Median Absolute Deviation (MAD)0.5
Skewness-0.94777029
Sum11775
Variance0.31759418
MonotonicityNot monotonic
2023-01-27T10:27:55.070546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4.5 1056
21.1%
4 975
19.5%
3.5 400
 
8.0%
5 228
 
4.6%
3 134
 
2.7%
2.5 58
 
1.2%
2 15
 
0.3%
1.5 4
 
0.1%
(Missing) 2130
42.6%
ValueCountFrequency (%)
1.5 4
 
0.1%
2 15
 
0.3%
2.5 58
 
1.2%
3 134
 
2.7%
3.5 400
 
8.0%
4 975
19.5%
4.5 1056
21.1%
5 228
 
4.6%
ValueCountFrequency (%)
5 228
 
4.6%
4.5 1056
21.1%
4 975
19.5%
3.5 400
 
8.0%
3 134
 
2.7%
2.5 58
 
1.2%
2 15
 
0.3%
1.5 4
 
0.1%

service
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.3%
Missing2101
Missing (%)42.0%
Infinite0
Infinite (%)0.0%
Mean4.0572611
Minimum1.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:55.147484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile3
Q13.5
median4
Q34.5
95-th percentile5
Maximum5
Range3.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.59413822
Coefficient of variation (CV)0.14643825
Kurtosis1.0854253
Mean4.0572611
Median Absolute Deviation (MAD)0.5
Skewness-0.83683526
Sum11762
Variance0.35300023
MonotonicityNot monotonic
2023-01-27T10:27:55.217845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 971
19.4%
4.5 949
19.0%
3.5 477
 
9.5%
5 247
 
4.9%
3 165
 
3.3%
2.5 60
 
1.2%
2 26
 
0.5%
1.5 4
 
0.1%
(Missing) 2101
42.0%
ValueCountFrequency (%)
1.5 4
 
0.1%
2 26
 
0.5%
2.5 60
 
1.2%
3 165
 
3.3%
3.5 477
9.5%
4 971
19.4%
4.5 949
19.0%
5 247
 
4.9%
ValueCountFrequency (%)
5 247
 
4.9%
4.5 949
19.0%
4 971
19.4%
3.5 477
9.5%
3 165
 
3.3%
2.5 60
 
1.2%
2 26
 
0.5%
1.5 4
 
0.1%

value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)0.3%
Missing2115
Missing (%)42.3%
Infinite0
Infinite (%)0.0%
Mean3.9785095
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:55.296643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13.5
median4
Q34.5
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.57969802
Coefficient of variation (CV)0.14570733
Kurtosis1.4521136
Mean3.9785095
Median Absolute Deviation (MAD)0.5
Skewness-0.87407755
Sum11478
Variance0.33604979
MonotonicityNot monotonic
2023-01-27T10:27:55.370542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 1090
21.8%
4.5 829
 
16.6%
3.5 530
 
10.6%
3 197
 
3.9%
5 146
 
2.9%
2.5 59
 
1.2%
2 28
 
0.6%
1.5 4
 
0.1%
1 2
 
< 0.1%
(Missing) 2115
42.3%
ValueCountFrequency (%)
1 2
 
< 0.1%
1.5 4
 
0.1%
2 28
 
0.6%
2.5 59
 
1.2%
3 197
 
3.9%
3.5 530
10.6%
4 1090
21.8%
4.5 829
16.6%
5 146
 
2.9%
ValueCountFrequency (%)
5 146
 
2.9%
4.5 829
16.6%
4 1090
21.8%
3.5 530
10.6%
3 197
 
3.9%
2.5 59
 
1.2%
2 28
 
0.6%
1.5 4
 
0.1%
1 2
 
< 0.1%

atmosphere
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.7%
Missing3812
Missing (%)76.2%
Infinite0
Infinite (%)0.0%
Mean3.9343434
Minimum1.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2023-01-27T10:27:55.448598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile3
Q13.5
median4
Q34.5
95-th percentile4.5
Maximum5
Range3.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.54678257
Coefficient of variation (CV)0.13897683
Kurtosis1.0043718
Mean3.9343434
Median Absolute Deviation (MAD)0.5
Skewness-0.75246363
Sum4674
Variance0.29897118
MonotonicityNot monotonic
2023-01-27T10:27:55.516918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 450
 
9.0%
4.5 325
 
6.5%
3.5 260
 
5.2%
3 90
 
1.8%
5 32
 
0.6%
2.5 21
 
0.4%
2 8
 
0.2%
1.5 2
 
< 0.1%
(Missing) 3812
76.2%
ValueCountFrequency (%)
1.5 2
 
< 0.1%
2 8
 
0.2%
2.5 21
 
0.4%
3 90
 
1.8%
3.5 260
5.2%
4 450
9.0%
4.5 325
6.5%
5 32
 
0.6%
ValueCountFrequency (%)
5 32
 
0.6%
4.5 325
6.5%
4 450
9.0%
3.5 260
5.2%
3 90
 
1.8%
2.5 21
 
0.4%
2 8
 
0.2%
1.5 2
 
< 0.1%

keywords
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct451
Distinct (%)100.0%
Missing4549
Missing (%)91.0%
Memory size78.1 KiB
tapas, canarian potatoes, baby squid, food was fantastic, nice restaurant
 
1
meatballs, burger, salad, duck, potatoes
 
1
burrata, pork, food and wine, dishes, asparagus
 
1
steak, chicken wings, prawns, the early bird menu, diner menu
 
1
burger, shepherd neame, the pub, landlord, dave
 
1
Other values (446)
446 

Length

Max length96
Median length73
Mean length55.002217
Min length31

Characters and Unicode

Total characters24806
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique451 ?
Unique (%)100.0%

Sample

1st rowdoughnuts, burger, tim bits, coffee place, great coffee
2nd rowbread, cakes, bakery, cinnamon, dough
3rd rowbeef, cakes, brothers who run, stayed here, post office
4th rowcurry, daal, poppadoms, rice, prawn puri
5th rowcheese balls, lamb, octopus, lovely food, staying nearby

Common Values

ValueCountFrequency (%)
tapas, canarian potatoes, baby squid, food was fantastic, nice restaurant 1
 
< 0.1%
meatballs, burger, salad, duck, potatoes 1
 
< 0.1%
burrata, pork, food and wine, dishes, asparagus 1
 
< 0.1%
steak, chicken wings, prawns, the early bird menu, diner menu 1
 
< 0.1%
burger, shepherd neame, the pub, landlord, dave 1
 
< 0.1%
mac and cheese, burger, sunday roast, the pub, roaring fire 1
 
< 0.1%
crispy duck, pancakes, chinese meal, food home, take away 1
 
< 0.1%
pizza, steak, tuna, prawns, ice cream 1
 
< 0.1%
homemade chips, pudding, cake, steak, sunday lunch 1
 
< 0.1%
steak, lamb shoulder, tapas, ribs, nice atmosphere 1
 
< 0.1%
Other values (441) 441
 
8.8%
(Missing) 4549
91.0%

Length

2023-01-27T10:27:55.626330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
food 84
 
2.3%
fish 65
 
1.8%
steak 64
 
1.8%
and 58
 
1.6%
salad 54
 
1.5%
great 48
 
1.3%
the 45
 
1.2%
pub 45
 
1.2%
bread 45
 
1.2%
pudding 44
 
1.2%
Other values (1067) 3081
84.8%

Most occurring characters

ValueCountFrequency (%)
3182
12.8%
e 2158
 
8.7%
a 2115
 
8.5%
, 1804
 
7.3%
s 1602
 
6.5%
r 1307
 
5.3%
i 1294
 
5.2%
o 1204
 
4.9%
t 1194
 
4.8%
n 1123
 
4.5%
Other values (19) 7823
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19817
79.9%
Space Separator 3182
 
12.8%
Other Punctuation 1807
 
7.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2158
 
10.9%
a 2115
 
10.7%
s 1602
 
8.1%
r 1307
 
6.6%
i 1294
 
6.5%
o 1204
 
6.1%
t 1194
 
6.0%
n 1123
 
5.7%
c 944
 
4.8%
l 815
 
4.1%
Other values (16) 6061
30.6%
Other Punctuation
ValueCountFrequency (%)
, 1804
99.8%
' 3
 
0.2%
Space Separator
ValueCountFrequency (%)
3182
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19817
79.9%
Common 4989
 
20.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2158
 
10.9%
a 2115
 
10.7%
s 1602
 
8.1%
r 1307
 
6.6%
i 1294
 
6.5%
o 1204
 
6.1%
t 1194
 
6.0%
n 1123
 
5.7%
c 944
 
4.8%
l 815
 
4.1%
Other values (16) 6061
30.6%
Common
ValueCountFrequency (%)
3182
63.8%
, 1804
36.2%
' 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3182
12.8%
e 2158
 
8.7%
a 2115
 
8.5%
, 1804
 
7.3%
s 1602
 
6.5%
r 1307
 
5.3%
i 1294
 
5.2%
o 1204
 
4.9%
t 1194
 
4.8%
n 1123
 
4.5%
Other values (19) 7823
31.5%

Interactions

2023-01-27T10:27:45.171261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:17.970043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:19.825520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:21.494590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:23.293210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:25.043151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:26.831063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:28.444957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:30.022034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:31.908379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:33.492024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:34.988363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:36.609357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:38.436636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:40.031923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:41.601317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:43.190225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:45.266386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:18.072654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:19.925458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:21.596077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:23.398079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:25.157416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:26.925714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:28.533159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:30.120581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:32.002698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:33.580605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:35.080398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:36.702891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:38.532597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:40.125380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:41.699904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:43.287984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:45.359744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:18.163386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:20.020904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:21.697564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:23.494161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:25.262755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:27.020041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:28.618232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:30.211939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:32.092228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:33.667115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:35.171129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:37.069135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:38.624280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:40.218246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:41.790918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:43.382054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:45.459405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:18.267619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:20.117908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:21.792126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:23.587403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:25.355935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:27.116292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:28.713763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:30.303389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:32.182266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:33.753827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:35.263978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-27T10:27:34.808655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:36.422210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:38.247247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:39.840447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:41.410260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:42.997370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:44.975694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:46.717018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:19.724438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:21.392563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:23.184048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:24.931513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:26.733081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:28.348843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:29.925866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:31.813557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:33.397752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:34.899467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:36.515588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:38.341706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:39.936253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:41.506470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:43.094931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T10:27:45.073758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-27T10:27:55.753662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
latitudelongitudeopen_days_per_weekopen_hours_per_weekworking_shifts_per_weekavg_ratingtotal_reviews_countreviews_count_in_default_languageexcellentvery_goodaveragepoorterriblefoodservicevalueatmospherecountryclaimedprice_levelspecial_dietsvegetarian_friendlyvegan_optionsgluten_freedefault_language
latitude1.000-0.0170.078-0.016-0.104-0.026-0.0520.1040.0690.1180.1120.1350.136-0.0210.015-0.0190.0370.5960.1070.0540.0760.0910.1180.1290.242
longitude-0.0171.0000.0160.053-0.0090.020-0.089-0.154-0.137-0.140-0.122-0.134-0.173-0.004-0.054-0.027-0.0320.6320.0590.0000.0820.0820.1090.1320.215
open_days_per_week0.0780.0161.0000.5490.452-0.2680.0220.1030.0350.1230.1770.1670.188-0.249-0.224-0.240-0.1570.1330.0910.0920.0210.0310.0560.0270.115
open_hours_per_week-0.0160.0530.5491.0000.017-0.179-0.0810.009-0.0350.0270.0970.0830.091-0.209-0.176-0.142-0.0870.1340.1120.1460.0380.0950.0370.0650.000
working_shifts_per_week-0.104-0.0090.4520.0171.000-0.1360.1820.029-0.0030.0720.0570.0440.050-0.091-0.114-0.134-0.1690.1410.0830.1050.0600.0680.0260.0650.103
avg_rating-0.0260.020-0.268-0.179-0.1361.000-0.072-0.0160.249-0.117-0.260-0.255-0.3810.7870.7840.7380.5930.0600.2290.1070.0580.2790.1840.2090.162
total_reviews_count-0.052-0.0890.022-0.0810.182-0.0721.0000.6280.5910.5890.5050.4620.4080.0370.006-0.0820.0390.0200.2180.0920.1710.2550.3290.4190.153
reviews_count_in_default_language0.104-0.1540.1030.0090.029-0.0160.6281.0000.9130.8530.7450.6920.6740.0560.0910.0390.1100.1010.1740.0520.1100.2150.3420.3850.104
excellent0.069-0.1370.035-0.035-0.0030.2490.5910.9131.0000.7000.5870.5610.5190.2480.2760.2120.2340.0910.1670.0540.1260.2120.3530.4000.103
very_good0.118-0.1400.1230.0270.072-0.1170.5890.8530.7001.0000.6870.6300.588-0.035-0.004-0.0330.0510.0880.1420.0500.0870.1840.3030.3420.084
average0.112-0.1220.1770.0970.057-0.2600.5050.7450.5870.6871.0000.6600.634-0.192-0.150-0.186-0.0450.0910.1440.0560.0640.1810.2740.3290.077
poor0.135-0.1340.1670.0830.044-0.2550.4620.6920.5610.6300.6601.0000.655-0.182-0.163-0.203-0.0520.0930.1420.0610.0910.1820.2650.3310.078
terrible0.136-0.1730.1880.0910.050-0.3810.4080.6740.5190.5880.6340.6551.000-0.254-0.224-0.257-0.1050.0750.0970.0660.0740.0920.1330.1560.058
food-0.021-0.004-0.249-0.209-0.0910.7870.0370.0560.248-0.035-0.192-0.182-0.2541.0000.7810.7830.5800.0540.1920.1440.0560.2660.2000.2100.057
service0.015-0.054-0.224-0.176-0.1140.7840.0060.0910.276-0.004-0.150-0.163-0.2240.7811.0000.7700.6220.0780.2080.1170.0330.2330.1850.2020.029
value-0.019-0.027-0.240-0.142-0.1340.738-0.0820.0390.212-0.033-0.186-0.203-0.2570.7830.7701.0000.5170.0600.1540.1450.0740.2220.1650.1640.094
atmosphere0.037-0.032-0.157-0.087-0.1690.5930.0390.1100.2340.051-0.045-0.052-0.1050.5800.6220.5171.0000.0080.2010.1600.0000.2250.1200.2000.088
country0.5960.6320.1330.1340.1410.0600.0200.1010.0910.0880.0910.0930.0750.0540.0780.0600.0081.0000.1960.0790.0890.2200.1930.2460.351
claimed0.1070.0590.0910.1120.0830.2290.2180.1740.1670.1420.1440.1420.0970.1920.2080.1540.2010.1961.0000.1540.3220.3410.2770.3250.187
price_level0.0540.0000.0920.1460.1050.1070.0920.0520.0540.0500.0560.0610.0660.1440.1170.1450.1600.0790.1541.0000.1480.1800.1130.1920.104
special_diets0.0760.0820.0210.0380.0600.0580.1710.1100.1260.0870.0640.0910.0740.0560.0330.0740.0000.0890.3220.1481.0000.9920.9920.9920.140
vegetarian_friendly0.0910.0820.0310.0950.0680.2790.2550.2150.2120.1840.1810.1820.0920.2660.2330.2220.2250.2200.3410.1800.9921.0000.5420.4640.329
vegan_options0.1180.1090.0560.0370.0260.1840.3290.3420.3530.3030.2740.2650.1330.2000.1850.1650.1200.1930.2770.1130.9920.5421.0000.5310.225
gluten_free0.1290.1320.0270.0650.0650.2090.4190.3850.4000.3420.3290.3310.1560.2100.2020.1640.2000.2460.3250.1920.9920.4640.5311.0000.225
default_language0.2420.2150.1150.0000.1030.1620.1530.1040.1030.0840.0770.0780.0580.0570.0290.0940.0880.3510.1870.1040.1400.3290.2250.2251.000

Missing values

2023-01-27T10:27:46.932483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-27T10:27:47.637040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-27T10:27:48.245494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

restaurant_linkrestaurant_nameoriginal_locationcountryregionprovincecityaddresslatitudelongitudeclaimedawardspopularity_detailedpopularity_generictop_tagsprice_levelprice_rangemealscuisinesspecial_dietsfeaturesvegetarian_friendlyvegan_optionsgluten_freeoriginal_open_hoursopen_days_per_weekopen_hours_per_weekworking_shifts_per_weekavg_ratingtotal_reviews_countdefault_languagereviews_count_in_default_languageexcellentvery_goodaveragepoorterriblefoodservicevalueatmospherekeywords
548780g186402-d20259720The Coffee Bar["Europe", "United Kingdom (UK)", "England", "West Midlands", "Birmingham"]EnglandWest MidlandsNaNBirminghamThe Oasis 110-114 Corporation Street Oasis Market, Birmingham B4 6SX England51.124720-2.740000UnclaimedNaN#45 of 99 Coffee & Tea in Birmingham#810 of 2452 places to eat in BirminghamCheap Eats, Cafe€2-€11NaNCafeNaNNaNNNNNaNNaNNaNNaN5.07.0English7.07.00.00.00.00.0NaNNaNNaNNaNNaN
474556g790189-d10396421El Griego["Europe", "Spain", "Valencian Country", "Province of Alicante", "Costa Blanca", "L'Alfas del Pi", "El Albir"]SpainValencian CountryProvince of AlicanteNaNCarrer Vivaldi, 11 Local 2, 03581 El Albir, L'Alfas del Pi Spain38.571880-0.067822ClaimedTravellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018#18 of 128 Restaurants in El Albir#20 of 144 places to eat in El AlbirMid-range, Mediterranean, European, Greek€€-€€€€9-€20NaNMediterranean, European, GreekVegetarian Friendly, Vegan OptionsNaNYYN{"Mon": ["13:30-23:00"], "Tue": ["13:30-23:00"], "Wed": [], "Thu": ["13:30-23:00"], "Fri": ["13:30-23:00"], "Sat": ["13:30-23:00"], "Sun": ["13:30-23:00"]}6.057.06.04.5134.0English32.016.013.01.02.00.04.54.54.5NaNNaN
973914g3236223-d23258847Restaurant Karavovrisi["Europe", "Greece", "Crete", "Heraklion Prefecture", "Kaloi Limenes"]GreeceCreteHeraklion PrefectureKaloi LimenesKaloi Limenes, Crete 704 00 Greece34.93194024.802190UnclaimedNaN#2 of 2 Restaurants in Kaloi Limenes#2 of 3 places to eat in Kaloi LimenesPizza, Seafood, Mediterranean, GreekNaNNaNNaNPizza, Seafood, Mediterranean, Greek, RussianNaNNaNNNNNaNNaNNaNNaN5.01.0All languages1.01.00.00.00.00.0NaNNaNNaNNaNNaN
958235g13091931-d13089780Aroma Cafe-Bar["Europe", "Greece", "Central Macedonia", "Thessaloniki Region", "Nea Madytos"]GreeceCentral MacedoniaThessaloniki RegionNea MadytosNea Madytos 57014 Greece40.62888023.562120ClaimedNaN#3 of 3 Restaurants in Nea Madytos#3 of 5 places to eat in Nea MadytosCheap Eats, Italian, GreekNaNNaNItalian, GreekNaNNaNNNNNaNNaNNaNNaN5.02.0All languages2.02.00.00.00.00.0NaNNaNNaNNaNNaN
834906g3793652-d14039519Vanity caffè["Europe", "Italy", "Sicily", "Province of Enna", "Valguarnera Caropepe"]ItalySicilyProvince of EnnaNaNVia Giuseppe Mazzini 122, 94019 Valguarnera Caropepe, Sicily Italy37.49303414.390515UnclaimedNaN#9 of 10 Restaurants in Valguarnera Caropepe#9 of 10 places to eat in Valguarnera CaropepeCheap Eats, Cafe, Fast foodNaNNaNCafe, Fast foodNaNNaNNNNNaNNaNNaNNaN5.02.0All languages2.02.00.00.00.00.0NaNNaNNaNNaNNaN
539530g186356-d13526221Jaylowz Cafe & Takeaway["Europe", "United Kingdom (UK)", "England", "Nottinghamshire", "Nottingham"]EnglandNottinghamshireNaNNottinghamStation Road 11 Acorn Centre, Nottingham NG16 4AF England53.019200-1.328970UnclaimedNaN#599 of 917 Restaurants in Nottingham#733 of 1275 places to eat in NottinghamNaNNaNNaNNaNNaNNaNNaNNNNNaNNaNNaNNaN5.02.0English2.02.00.00.00.00.0NaNNaNNaNNaNNaN
34887g187111-d3423656Au Moulin a Vent["Europe", "France", "Bourgogne-Franche-Comte", "Cote d'Or", "Dijon"]FranceBourgogne-Franche-ComteCote d'OrDijon8 Place Francois Rude, 21000, Dijon France47.3224375.039159UnclaimedNaN#463 of 497 Restaurants in Dijon#506 of 586 places to eat in DijonMid-range, French, European€€-€€€NaNLunch, DinnerFrench, EuropeanNaNReservations, Outdoor Seating, Seating, Wheelchair Accessible, Serves Alcohol, Full Bar, Accepts Credit Cards, Table ServiceNNNNaNNaNNaNNaN3.0352.0English81.05.017.031.015.013.03.03.03.03.0NaN
610936g4922900-d9803410Old Bush["Europe", "United Kingdom (UK)", "England", "Staffordshire", "Wombourne"]EnglandStaffordshireNaNWombourneHigh Street, Wombourne WV5 9DT England52.536820-2.180818ClaimedNaN#9 of 12 Restaurants in Wombourne#11 of 15 places to eat in WombourneCheap EatsNaNDrinksNaNNaNReservations, Outdoor Seating, Buffet, Parking Available, Free off-street parking, Television, Highchairs Available, Wheelchair Accessible, Serves Alcohol, Full Bar, Wine and Beer, Free Wifi, Accepts Credit CardsNNN{"Mon": ["11:00-22:00"], "Tue": ["11:00-22:00"], "Wed": ["11:00-22:00"], "Thu": ["11:00-22:00"], "Fri": ["11:00-22:00"], "Sat": ["11:00-22:00"], "Sun": ["12:00-22:00"]}7.076.07.04.510.0English10.07.02.00.01.00.0NaNNaNNaNNaNNaN
233754g187342-d17295079Fullepavillon["Europe", "Germany", "Hesse", "Kassel"]GermanyHesseNaNKasselLeipziger Str. 2-4, 34125 Kassel, Hesse Germany51.3140609.508180UnclaimedNaN#189 of 298 Restaurants in Kassel#204 of 397 places to eat in KasselCheap EatsNaNNaNNaNNaNNaNNNNNaNNaNNaNNaN4.04.0All languages4.01.02.01.00.00.0NaNNaNNaNNaNNaN
667967g1080461-d5821803PTP Trattoria Del Convento["Europe", "Italy", "Piedmont", "Province of Cuneo", "Barge"]ItalyPiedmontProvince of CuneoNaNVia Montebracco 63, 12032 Barge Italy44.6993907.341670UnclaimedNaN#12 of 15 Restaurants in Barge#14 of 22 places to eat in BargeItalianNaNNaNNaNItalianNaNNaNNNNNaNNaNNaNNaN4.54.0All languages4.02.01.01.00.00.0NaNNaNNaNNaNNaN
restaurant_linkrestaurant_nameoriginal_locationcountryregionprovincecityaddresslatitudelongitudeclaimedawardspopularity_detailedpopularity_generictop_tagsprice_levelprice_rangemealscuisinesspecial_dietsfeaturesvegetarian_friendlyvegan_optionsgluten_freeoriginal_open_hoursopen_days_per_weekopen_hours_per_weekworking_shifts_per_weekavg_ratingtotal_reviews_countdefault_languagereviews_count_in_default_languageexcellentvery_goodaveragepoorterriblefoodservicevalueatmospherekeywords
194783g1081277-d8719990Ddm Grill["Europe", "Germany", "Hesse", "Maintal"]GermanyHesseNaNMaintalGoethestr. 4, 63477 Maintal, Hesse Germany50.1514608.810350UnclaimedNaN#14 of 34 Restaurants in Maintal#14 of 37 places to eat in MaintalNaNNaNNaNNaNNaNNaNNaNNNNNaNNaNNaNNaN4.57.0English1.01.00.00.00.00.0NaNNaNNaNNaNNaN
250851g187402-d21319326Eiscafé Gran Gelato["Europe", "Germany", "Saxony", "Zwickau"]GermanySaxonyNaNZwickauPeter-Breuer-Str. 37, 08056 Zwickau, Saxony Germany50.71857012.493380UnclaimedNaN#1 of 1 Dessert Spots in Zwickau#74 of 154 places to eat in ZwickauDessertNaNNaNNaNNaNNaNNaNNNN{"Mon": ["10:00-19:00"], "Tue": ["10:00-19:00"], "Wed": ["10:00-19:00"], "Thu": ["10:00-19:00"], "Fri": ["10:00-19:00"], "Sat": ["10:00-19:00"], "Sun": ["11:00-19:00"]}7.062.07.01.01.0All languages1.00.00.00.00.01.0NaNNaNNaNNaNNaN
944945g230021-d21184335Restaurant Mascotte["Europe", "Belgium", "Flanders", "West Flanders Province", "Knokke-Heist"]BelgiumFlandersWest Flanders ProvinceNaNZeedijk-Albertstrand 507 Grand Casino Knokke, Knokke-Heist 8300 BelgiumNaNNaNUnclaimedNaN#102 of 130 Restaurants in Knokke-Heist#185 of 260 places to eat in Knokke-HeistFrench, Belgian, Seafood, InternationalNaNNaNNaNFrench, Belgian, Seafood, International, EuropeanNaNNaNNNNNaNNaNNaNNaN4.01.0All languages1.00.01.00.00.00.0NaNNaNNaNNaNNaN
933184g188636-d11325898Het Rooi["Europe", "Belgium", "Flanders", "Antwerp Province", "Antwerp"]BelgiumFlandersAntwerp ProvinceNaNBerchemstadionstraat 73, Antwerp 2600 Belgium51.1882674.437908ClaimedNaN#466 of 1281 Restaurants in Antwerp#516 of 1572 places to eat in AntwerpMid-range, Belgian, Dutch, European€€-€€€NaNLunch, DinnerBelgian, Dutch, EuropeanVegetarian FriendlyNaNYNN{"Mon": ["10:30-22:00"], "Tue": ["10:30-22:00"], "Wed": ["10:30-22:00"], "Thu": ["10:30-22:00"], "Fri": ["10:30-22:00"], "Sat": ["10:30-22:00"], "Sun": ["10:30-22:00"]}7.080.57.04.035.0English3.01.01.01.00.00.04.04.04.0NaNNaN
789838g194813-d2316284Locanda dei Briganti["Europe", "Italy", "Tuscany", "Province of Grosseto", "Grosseto", "Marina di Grosseto"]ItalyTuscanyProvince of GrossetoNaNVia della Corvetta 8, 58100 Marina di Grosseto, Grosseto Italy42.72083310.999189ClaimedNaN#3 of 73 Restaurants in Marina di Grosseto#4 of 85 places to eat in Marina di GrossetoMid-range, Italian, Mediterranean, Barbecue€€-€€€€15-€35NaNItalian, Mediterranean, Barbecue, Tuscan, Central-ItalianGluten Free OptionsNaNNNY{"Mon": [], "Tue": ["19:30-23:30"], "Wed": ["19:30-23:30"], "Thu": ["19:30-23:30"], "Fri": ["19:30-23:30"], "Sat": ["19:30-23:30"], "Sun": ["12:30-14:30"]}6.022.06.04.5574.0English2.00.00.00.01.01.04.04.04.03.5NaN
989156g294454-d14118204Namaste India["Europe", "Croatia", "Central Croatia", "Zagreb"]CroatiaCentral CroatiaNaNZagrebSelska Cesta 217, Zagreb 10000 Croatia45.78680415.949535ClaimedTravellers' Choice, Certificate of Excellence 2020#51 of 756 Restaurants in Zagreb#57 of 976 places to eat in ZagrebMid-range, Indian, Asian, Vegetarian Friendly€€-€€€€3-€11NaNIndian, AsianVegetarian Friendly, Vegan Options, Gluten Free OptionsNaNYYY{"Mon": ["14:00-23:00"], "Tue": ["00:00-23:00"], "Wed": ["00:00-23:00"], "Thu": ["00:00-23:00"], "Fri": ["00:00-23:00"], "Sat": ["00:00-23:00"], "Sun": ["00:00-22:00"]}7.0146.07.04.5107.0English97.075.012.04.03.03.04.55.04.5NaNnaan, rice, biryani, lamb, indian restaurant
237465g187357-d10701337Happy S Bar["Europe", "Germany", "Lower Saxony", "Wolfsburg"]GermanyLower SaxonyNaNWolfsburgKaufhof 13, 38440 Wolfsburg, Lower Saxony Germany52.42356010.784670UnclaimedNaNNaNNaNCheap Eats, Cafe, Pub, Wine Bar€3-€6NaNCafe, Pub, Wine Bar, Gastropub, ArabicNaNNaNNNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
93532g196497-d4337396L'Original - saveurs d'Asie["Europe", "France", "Grand Est", "Bas-Rhin", "Obernai"]FranceGrand EstBas-RhinObernai117 rue du General Gouraud, 67210, Obernai France48.4617277.477758UnclaimedCertificate of Excellence 2019, Certificate of Excellence 2017, Certificate of Excellence 2016, Certificate of Excellence 2015#14 of 55 Restaurants in Obernai#14 of 71 places to eat in ObernaiMid-range, Asian€€-€€€NaNLunch, DinnerAsianNaNReservations, Seating, Serves Alcohol, Table ServiceNNN{"Mon": ["12:00-14:00", "19:00-22:00"], "Tue": ["12:00-14:00", "19:00-22:00"], "Wed": [], "Thu": ["12:00-14:00", "19:00-22:00"], "Fri": ["12:00-14:00", "19:00-22:00"], "Sat": ["19:00-22:00"], "Sun": ["12:00-14:00", "19:00-22:00"]}6.028.011.04.5165.0English2.02.00.00.00.00.04.54.54.04.0NaN
193708g1063629-d11925695Schumann`s Genusswerkstatt["Europe", "Germany", "Saxony", "Pulsnitz"]GermanySaxonyNaNPulsnitzKastanienweg 7, 01896 Pulsnitz, Saxony Germany51.17764314.013830UnclaimedNaN#1 of 4 Restaurants in Pulsnitz#1 of 5 places to eat in PulsnitzMid-range, German, Vegetarian Friendly, Gluten Free Options€€-€€€NaNLunch, DinnerGermanVegetarian Friendly, Gluten Free OptionsNaNYNY{"Mon": [], "Tue": ["17:00-22:00"], "Wed": ["12:00-22:00"], "Thu": ["12:00-22:00"], "Fri": ["12:00-22:00"], "Sat": ["12:00-23:00"], "Sun": ["11:00-18:00"]}6.053.06.04.553.0English2.02.00.00.00.00.04.54.54.0NaNNaN
300903g1055475-d14193108Sunn Alm Gerlitzen["Europe", "Austria", "Austrian Alps", "Carinthia", "Treffen"]AustriaAustrian AlpsCarinthiaTreffenKanzelhöhe, Treffen 9521 Austria46.68166413.900064ClaimedNaN#9 of 10 Restaurants in Treffen#10 of 11 places to eat in TreffenMid-range, Italian, Austrian, International€€-€€€€6-€15NaNItalian, Austrian, International, EuropeanNaNReservationsNNNNaNNaNNaNNaN4.09.0English2.00.02.00.00.00.0NaNNaNNaNNaNNaN